Previous seminars

L. Leskelä - Clustering coefficients in large directed graphs

Lasse Leskelä (Aalto)

Thursday 2017-05-04 14.15 - 15.00

Room A136 (T6), T-building

Clustering coefficients in large directed graphs


I will discuss a notion of clustering in directed graphs which describes how likely two followers of a node are to follow a common target. The associated network motifs, called dicliques or bi-fans, have been found to be key structural components in various real-world networks. A two-mode statistical network model consisting of actors and auxiliary attributes is introduced, where an actor i decides to follow an actor j whenever i demands an attribute supplied by j. This directed random graph model admits nontrivial clustering properties of the aforementioned type, as well as power-law indegree and outdegree distributions. The talk is based on joint work with Mindaugas Bloznelis (U Vilnius), available at arXiv:1607.02278.


J. Autere - EIT Digital's Industrial Doctoral Program

Jussi Autere (Aalto)

Thursday 2017-04-06 14.00 - 15.00

Room A136 (T6), T-building

EIT Digital's Industrial Doctoral Program


EIT Digital a leading European digital innovation and entrepreneurial education organisation has started an industrial doctoral program. It offers doctoral student positions to researchers interested in subjects relevant to the industry.


A. Ghosh - Finnish migration patterns: Proximity with parents and siblings across the life course

Asim Ghosh (Aalto)

Thursday 2017-03-30 14.00 - 15.00

Room A136 (T6), T-building

Finnish migration patterns: Proximity with parents and siblings across the life course


In order to get insight into the migration patterns of people across their life course and how they are separating from their parents and siblings, we analyse their migration dynamics from one region (Maakunta) to another region in Finland. Finland is divided into 19 regions or Maakuntas and first we draw the flow of people moving from one region to another. We observe that the migration probability of people is higher at their early infancy due to their parents moving, but they also migrate with high probability at their early adulthood for their education or for jobs in different regions. We calculate the probability for staying with parents as a function of the age of children and find that when the childen's age is from 15 to 30 years they have a greater probability to move out from their parents' home. Comparing sons with daughters reveals that the daughters start to move out from their parents earlier and continue to do so with higher rate. We also study the cohabitation probability with siblings and half-siblings and find that the probability for two full sibling brothers to be in the same region is the highest in comparison with the other types of siblinghood pairs. Overall, we find that in Finnish society the females have larger probability to migrate than males do.


A. Jung - When is Network Lasso Accurate?

Alex Jung (Aalto)

Thursday 2017-04-13 14.00 - 15.00

Room A136 (T6), T-building

When is Network Lasso Accurate?


The network LASSO is a recently proposed method for clustering and optimization problems arising from massive network-structured datasets, i.e.,
for big data over networks. It is a variant of the well-known group LASSO which is underlying many methods in machine learning, statistics and signal processing involving sparsity assumptions. While much work has been devoted to studying efficient and scalable implementations, only little is known about conditions on the underlying network structure required by network Lasso to be accurate. In some of our most recent work we close this gap by giving precise conditions on the underlying network topology which guarantee the network lasso to be accurate.


Journal Club - Network Lasso: Clustering and Optimization in Large Graphs

March 9, 2017 @ 14.00 - 15.00
Room A136 (T6), T-building
Presenter: Jonathan Strahl

Authors: David Hallac, Jure Leskovec, Stephen Boyd

Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimization solvers do not scale well, and scalable solvers are often specialized to only work on a narrow class of problems. Therefore, there is a need for simple, scalable algorithms that can solve many common optimization problems. In this paper, we introduce the network lasso, a generalization of the group lasso to a network setting that allows for simultaneous clustering and optimization on graphs. We develop an algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in a distributed and scalable manner, which allows for guaranteed global convergence even on large graphs. We also examine a non-convex extension of this approach. We then demonstrate that many types of problems can be expressed in our framework. We focus on three in particular — binary classification, predicting housing prices, and event detection in time series data — comparing the network lasso to baseline approaches and showing that it is both a fast and accurate method of solving large optimization problems.

Download the article here
Presentation slides


P. della Briotta Parolo - Scientific Snowballs

Pietro della Briotta Parolo (Aalto)

Monday 2016-12-12 11.00 - 12.00

Lecture hall AS3, TUAS building

Scientific Snowballs


Our idea is to analyze the spread of scientific ideas coming from a certain field/subfield in time following the chain of citations that follow the initial publications. The basic case study is based on initializing a set N of papers from a certain field/subfield/journal and year and assigning to each an initial scientific value,which can be initialized either equally (i.e. 1/N for each) or based on the citations each paper received (more value to more influential papers). Then we proceed to push the initial value through the network until the desired year is reached. This is done by organizing the network in topological order and looping through the ordered network, thus making sure the values are always pushed forward. Similarly to the initialization of value, the pushing can be performed either equally (each child paper receives an equal amount of value) or based on citations (each child papers gets value proportionally to its number of citations). At the end of the pushing, the value is gathered from paper level to field/subfield/journal level.

The initial results show that all fields follow a similar behavior, retaining most of their scientific value, but losing it quickly in the first years and more gradually as time goes by, eventually reaching a plateau. On the other hand the distribution of values within the field seems to keep the same distribution of value across it subfields. The next step is to quantify the rate of loss of value within a field and try to compare the value loss across fields and time.


J. Rousu - Predicting structured data

Juho Rousu (Aalto)

Monday 2016-12-05 11.00 - 12.00

Lecture hall AS3, TUAS building

Predicting structured data


In modern data analysis, data does not always come in neat tables of numbers, each column corresponding to a variable of interest. In particular, many data have internal structure, with statistical dependencies arising between close parts of the structure. In my talk I will introduce structured (output) prediction, a branch of machine learning that aims to take advantage of the data structures (e.g. sequences, trees, networks) and the embedded statistical dependencies, in order to make predictive models more accurate and inference more efficient. I will illustrate the methods by two examples: Network response prediction (Su et al, ICML 2014), that asks to predict a subnetwork that responds to a stimulus arising in a node of a larger network, and Metabolite identification (Brouard et al. 2016, ISMB 2016), where the outputs correspond to graphs representing small molecules.


K. Bhattacharya - Network of families resulting from unions in the FINNFAMILY data

Kunal Bhattacharya (Aalto)

Monday 2016-11-07 11.00 - 12.00

Lecture hall AS3, TUAS building

Network of families resulting from unions in the FINNFAMILY dat


The FINNFAMILY dataset is a multi-generational representative dataset from late 20th century Finland. The data is derived from the National Population Register of Finland and consists of 60,000 randomly selected Finns (index-persons) from six birth cohorts. The dataset allows to sample "unions" (male-female pair who are not known to be genetically related and whose union resulted in one or more offspring) between extended families. Analysis of these linkages will show that “social gravity” plays an important role in structuring the network between families. In addition, the analysis will also indicate the overall patterns of kinship in contemporary Finland.


S. Heydari - Social Signatures

Sara Heydari (Aalto)

Monday 2016-10-31 11.00 - 12.00

Lecture hall AS3, TUAS building

Social Signatures


Humans have limited time and resources for socializing. People do not distribute these limited resources evenly between all their acquaintance but they dedicate most of their communication effort to a few alters while distributing the rest between numerous alters. Although this disparity exists in communication patterns of all individuals, there are individual differences between patterns. Social signatures [1], which are communication patterns constructed from mobile phone records, can reflect both the general disparity in communication and individual differences in the ways how people prefer to shape their networks. In this talk we will first review past research works on social signatures, and then explore possible relationships between the shape of social signatures and characteristic attributes of individuals, networks attributes(degree, centrality) and homophily. We also talk about differences and similarities of social signatures constructed from communication data through different channels (phone calls and text messages).

[1] Saramäki, Jari, et al. "Persistence of social signatures in human communication." Proceedings of the National Academy of Sciences 111.3 (2014): 942-947.


D. Monsivais-Velazquez - Seasonal, geographical and thermal influence on human resting patterns inferred from mobile phone data

Daniel Monsivais-Velazquez (Aalto)

Monday 2016-10-24 11.00 - 12.00

Lecture hall AS3, TUAS building

Seasonal, geographical and thermal influence on human resting patterns inferred from mobile phone data


We study influence of seasonally and geographically related daily dynamics of daylight and daily temperature on human resting or sleeping patterns using mobile phone data of a very large number of individuals. We characterize the two observed daily "resting" or inactivity periods for calling in the aggregated pattern of mobile phone using population, inferring it to represent the sleeping times of people in cities. We find that the nocturnal resting period is strongly influenced by the length of the daylight, and that its seasonal variation depends on latitude, such that for people living in cities lying in two parallels separated by eight degrees, the difference of the resting period between summer and winter for southern cities is almost twice the value for the northern cities. We also observe that the duration of the resting period taking place in the afternoons is influenced by the temperature, and that there is a threshold from which this influence sets on. Finally, we argue that yearly dynamics of the afternoon and nocturnal resting periods are entangled, in such a way that the total daily resting period is constant along the year.

Article in arXiv: https://arxiv.org/abs/1607.06341


D. Ferreira - Understanding human behaviour with technology: challenges, tools & methods

Denzil Ferreira (University of Oulu)

Monday 2016-11-01 12.00 - 13.00

Lecture hall AS3, TUAS building

Understanding human behaviour with technology: challenges, tools & methods


Mobile phones have an increasing spectrum of built-in sensors, such as motion, light, atmospheric pressure. These sensors are primarily used to enhance the user experience with the device, such as detecting the screen orientation. More important for scientists, these sensors offer the potential to sense and reason about the user’s environment, or in other words, the user’s context. Mobile phones are the most widespread personal sensing device and provide an exciting opportunity for wider cross-disciplinary research to attain a better understanding of human behaviour by analysing the users’ unique context.

Yet the biggest challenge in conducting user studies is the scientists’ need to build software and logging tools from scratch, often without proper development experience, over and over again. More critically, multidisciplinary research becomes increasingly challenging due to the diversity of applications and environments. Researchers have no infrastructure support for exchanging their expertise and to collaborate locally or remotely.

In this talk, we introduce AWARE, a tool that focuses on an infrastructure for sensing behavioural and social context from mobile phones sensors, to enable a better understanding of human and social behaviour, and to improve users’ understanding of their own quality of life. More importantly, it is a platform that supports reuse and sharing of mobile-based behavioural and social context and researchers’ expertise.


Ferreira’s research is on leveraging mobile technologies to improve people’s lives, understanding people’s frustrations and fix them! He develops and evaluate tools for better understanding how mobility and social context affects your wellbeing and others. He is a member of Center for Ubiquitous Computing (http://ubicomp.oulu.fi) research group at the University of Oulu, and also a member of Ubicomp Lab research group at the Human-Computer Interaction Institute at Carnegie Mellon University.


T. Alakörkkö - Monitoring daily behavioural patterns using mobile phone sensors and ballistocardiography for detection of mental health changes

Tuomas Alakörkkö (Aalto)

Monday 2016-10-17 11.00 - 12.00

Lecture hall AS3, TUAS building

Monitoring daily behavioural patterns using mobile phone sensors and ballistocardiography for detection of mental health changes


Increasing number of mental health problems in population have created a need for developing new patient monitoring methods for mental health care. Use of different type of sensors could automatize the monitoring which could help to provide mental health services for all in need with the limited resources. In these days, commonly used mobile phones have sensors which can be used to collect data related to human behaviour unobtrusively. These sensors can measure for example acceleration, ambient noise levels, brightness and usage of a mobile phone. Also, a ballistocardiographic sensor can be used to obtain information about rest which is known to be a factor in mental health.
A pilot study was conducted where mobile phone sensors and ballistocardiographic sensor were used to measure sixteen subjects for six weeks. Subjects also filled daily questionnaires related to mental health and sleep and wore an actigraph for two weeks which were used as a comparison for the passively collected data. This thesis presents and and evaluates methods for the analysis of the sensor data. These methods could be used in future studies to detect differences between mental health patients and normal population and to search for markers of mental health problems.


D. Hric - Network structure, metadata, and the prediction of missing nodes and annotations

Darko Hric (Aalto)

Monday 2016-09-12 11.00 - 12.00

Lecture hall AS3, TUAS building

Network structure, metadata, and the prediction of missing nodes and annotations


The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure. Most often, the suitability of the annotations as topological descriptors itself is not assessed, and without this it is not possible to ultimately distinguish between actual shortcomings of the community detection algorithms, on one hand, and the incompleteness, inaccuracy, or structured nature of the data annotations themselves, on the other. In this work we present a principled method to access both aspects simultaneously. We construct a joint generative model for the data and metadata, and a non-parametric Bayesian framework to infer its parameters from annotated data sets. We assess the quality of the metadata not according to its direct alignment with the network communities, but rather in its capacity to predict the placement of edges in the network. We also show how this feature can be used to predict the connections to missing nodes when only the metadata are available. By investigating a wide range of data sets, we show that while there are seldom exact agreements between metadata tokens and the inferred data groups, the metadata are often informative of the network structure nevertheless, and can improve the prediction of missing nodes. This shows that the method uncovers meaningful patterns in both the data and metadata, without requiring or expecting a perfect agreement between the two.


J. Török - Understanding and coping with extremism in an online collaborative environment

János Török (Budapest University of Technology)

Monday 2016-08-15 11.00 - 12.00

Lecture hall AS3, TUAS building

Understanding and coping with extremism in an online collaborative environment


The Internet has provided us with great opportunities for large scale collaborative public good projects. Wikipedia is a predominant example of such projects where conflicts emerge and get resolved through bottom-up mechanisms leading to the emergence of the largest encyclopedia in human history. Disaccord arises whenever editors with different opinions try to produce an article reflecting a consensual view. The debates are mainly heated by editors with extremist views. Using a model of common value production, we show that the consensus can only be reached if extremist groups can actively take part in the discussion and if their views are also represented in the common outcome, at least temporarily. We show that banning problematic editors mostly hinders the consensus as it delays discussion and thus the whole consensus building process. To validate the model, relevant quantities are measured both in simulations and Wikipedia which show satisfactory agreement. We also consider the role of direct communication between editors both in the model and in Wikipedia data (by analysing the Wikipedia talk pages). While the model suggests that in certain conditions there is an optimal rate of "talking" vs "editing", it correctly predicts that in the current settings of Wikipedia, more activity in talk pages is associated with more controversy.


H.-H. Jo - Burstiness parameter for finite event sequences

Hang-Hyun Jo (POSTECH/Aalto)

Monday 2016-08-08 11.00 - 12.00

Lecture hall AS3, TUAS building

Burstiness parameter for finite event sequences


Characterizing inhomogeneous temporal patterns in natural and social phenomena is important to understand underlying mechanisms behind such complex systems, hence even to predict and control them. Temporal inhomogeneities in event sequences have been described in terms of bursts that are rapidly occurring events in short time periods alternating with long inactive periods. The bursts can be quantified by a simple measure, called burstiness parameter, which was introduced by Goh and Barab\'asi [EPL \textbf{81}, 48002 (2008)]. The burstiness parameter has been widely used due to its simplicity, which however turns out to be strongly biased when the number of events in the time series is not large enough. As the finite size effects on burstiness parameter have been largely ignored, we analytically investigate the finite size effects of the burstiness parameter. Then we suggest an alternative definition of burstiness parameter that is unbiased and yet simple. Using our alternative burstiness parameter, one can distinguish the finite size effects from the intrinsic bursty properties in the time series. We also demonstrate the advantages of our burstiness parameter by analyzing empirical datasets.

Article: http://arxiv.org/abs/1604.01125​


T. Shimada - A transition in growth and robustness of evolving networks

Takashi Shimada (Department of Applied Physics, The University of Tokyo)

Monday 2016-08-01 11.00 - 12.00

Lecture hall AS3, TUAS building

A transition in growth and robustness of evolving networks


An important and universal feature of real complex systems, such as social, economic, engineering, ecological, and biological systems, is that those are open: in those systems, constituting elements are not fixed and the complexity emerges (at least persist) under successive introductions of new elements. Those systems sometimes grow, but also sometimes collapse. Therefore why and when, in general, we can have such open and complex systems is a fundamental question. In this talk, we revisit this classical problem using our very simple graph dynamics model. I will show that this model gives either continuous growth or stagnation in system size, depending on the model’s unique parameter: the average number of weighted links per node m. The system can grow only if the connection is moderately sparse, i.e. 5 m 18. We can further find that this transition originates from an essential balance of two effects: although having more interactions makes each node robust, it also increases the impact of the loss of a node [1, 2]. This novel relation might be a origin of the moderately sparse (average degree 10) network structure ubiquitously found in real world, and the non-trivial distribution function of the lifetime of elements [3].

[1] T. Shimada, in Springer monograph Mathematical Approaches to Biological Systems: networks, Oscillations and Collective Motions (Springer, 2015) p. 95-117.
[2] T. Shimada “ A universal transition in the robustness of evolving open systems ”, Scientific Reports Vol. 4, 4082 (2014).
[3] Y. Murase, T. Shimada, & Ito, N.“A simple model for skewed species-lifetime distributions ”, New J. of Physics 12, 063021 (2010).


Y. Murase - What does Big Data tell? Sampling the social network by communication channels

Yohsuke Murase (RIKEN Advanced Institute for Computational Science)

Wednesday 2016-08-10 11.00 - 12.00

Lecture hall AS3, TUAS building

What does Big Data tell? Sampling the social network by communication channels


Big Data has become the primary source of understanding the structure and dynamics of the society at large scale. The network of social interactions can be considered as a multiplex, where each layer corresponds to one communication channel and the aggregate of all them constitutes the entire social network. However, usually one has information only about one of the channels or even a part of it, which should be considered as a sample of the whole. Here we show by simulations and analytical methods that this sampling may lead to bias. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get with reasonable assumptions about the sampling method a monotonously decreasing distribution as observed in empirical studies of single channel data. We also find that assortativity may occur or get strengthened due to the sampling method. We analyze the far-reaching consequences of our findings.

Article: http://arxiv.org/abs/1511.08749, J. Torok, Y. Murase, H.-H. Jo, J. Kertesz, K. Kaski "What does Big Data tell? Sampling the social network by communication channels".


G. Iñiguez - Global contagion with local cascades: Modelling the slow adoption of technology online

Gerardo Iñiguez (CIDE, Mexico/Aalto)

Monday 2016-07-04 11.00 - 12.00

Lecture hall AS3, TUAS building

Global contagion with local cascades: Modelling the slow adoption of technology online


Adoption of innovations, products or online services is commonly interpreted as a spreading process driven to large extent by social influence and conditioned by the needs and capacities of individuals. To model this process one usually introduces behavioural threshold mechanisms, which can give rise to the evolution of global cascades if the system satisfies a set of conditions. However, these models do not address temporal aspects of the emerging cascades, which in real systems may evolve through various pathways ranging from slow to rapid patterns. Here we fill this gap through the analysis and modelling of product adoption in the world’s largest voice over internet service, the social network of Skype. We provide empirical evidence about the heterogeneous distribution of fractional behavioural thresholds, which appears to be independent of the degree of adopting egos. We show that the structure of real-world adoption clusters is radically different from previous theoretical expectations, since vulnerable adoptions—induced by a single adopting neighbour—appear to be important only locally, while spontaneous adopters arriving at a constant rate and the involvement of unconcerned individuals govern the global emergence of social spreading.

Article in Sci. Rep.: http://www.nature.com/articles/srep27178
Press release in Aalto: http://cs.aalto.fi/en/current/news/2016-06-29-002/


Journal club on fast route planning algorithms

June 20, 2016 @ 11.00 - 12.00
Lecture room AS3, TUAS
Presenter: Rainer Kujala

Approximate outline of the talk:
1. Graph search basics and terminology
2. (Traditional) Speed-up techniques for static graphs
3. Modern techniques for routing in road (=static networks)
4. Time-dependent road network routing (traffic jams, changing road conditions...)
5. Routing in public transport networks

Based mostly on the article:

Route Planning in Transportation Networks

Authors: Hannah Bast, Daniel Delling, Andrew Goldberg, Matthias Müller-Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, Renato F. Werneck

Abstract: We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.

Download the article here


B. Monastero - Underlined Places. Augmenting a Lobby Floor to Unveil Mobility Patterns and Relations to the Inhabited Space

Beatrice Monastero (Aalto)

Monday 2016-06-13 11.00 - 12.00

Lecture hall AS3, TUAS building

Underlined Places. Augmenting a Lobby Floor to Unveil Mobility Patterns and Relations to the Inhabited Space


I will introduce Underlined Places (UP), an interactive floor projection installed in the lobby of a building. UP displays the mobility patterns of visitors in the building as white traces appearing in real-time on the floor when visitors walk by. Through a 6 week in-field study I investigated how intentional and unintentional interactions with the UP affected awareness of social aspects of the space related to people presence and behaviours. I will present the study and the related results outlining how uses and understanding of the system evolved in time. Three distinct phases of use emerged, characterised by different degrees of appropriation and different attitudes towards the augmented environment of both visitors and employees. Finally I outline how the public reactive visualisation of mobility patterns enhanced social awareness and supported the felt identity of the place (placefulness) and suggest similar urban design interventions.


A. Gabrielli - The Scientific Competitiveness of Nations: a network analysis

Andrea Gabrielli (CNR, Rome)

Monday 2016-06-14 13.00 - 14.00

Lecture hall AS3, TUAS building

The Scientific Competitiveness of Nations: a network analysis

Authors: Andrea Gabrielli, Giulio Cimini, Francesco Sylos Labini, Andrea Zaccaria


We use citation data of scientific articles produced by individual nations in different scientific domains to build a bipartite country - scientific domains network to determine the structure and efficiency of national research systems [1]. We characterize the scientific fitness of each nation—that is, the competitiveness of its research system—and the complexity of each scientific domain by means of a non-linear iterative algorithm [2] able to assess quantitatively the advantage of scientific diversification. We find that technological leading nations, beyond having the largest production of scientific papers and the largest number of citations, do not specialize in a few scientific domains. Rather, they diversify as much as possible their research system. On the other side, less developed nations are competitive only in scientific domains where also many other nations are present. Diversification thus represents the key element that correlates with scientific and technological competitiveness. A remarkable implication of this structure of the scientific competition is that the scientific domains playing the role of “markers” of national scientific competitiveness are those not necessarily of high technological requirements, but rather addressing the most ‘‘sophisticated’’ needs of the society. We complement this analysis with a correlation study between the scientific impact of a nation with a normalized measure of RD funds and the level of internationalization [3].

[1] G. Cimini, A. Gabrielli, F. Sylos Labini (2014), PLoS ONE 9(12), e113470.

[2] A. Tacchella et al. (2013), Sci. Rep. 2, 723.

[3] G. Cimini, A. Zaccaria, A. Gabrielli (2016), J. of Informetrics 10, 200.

Abstract for the talk.


S. Ranganathan - Dynamics of Correlated Investors Network

Sindhuja Ranganathan (TUT)

Monday 2016-06-06 11.00 - 12.00

Lecture hall AS3, TUAS building

Dynamics of Correlated Investors Network


Complex networks methodology has been used in the past to analyse stock markets by analysing correlation networks constructed between stocks. Networks constructed between investors have received considerably less attention in the literature perhaps because of the lack of suitable data sources. In this talk I will outline how to construct and analyse investor networks from large-scale data on Finnish stock markets. The main objective of this research is to analyse the change of the investor behaviour around financial crises, and I will go through some of my preliminary results on longitudinal analysis of the investor networks.


K. Bhattacharya - Quantifying social compensation under risks of relationship weakening

Kunal Bhattacharya (Aalto)

Monday 2016-05-23 11.00 - 12.00

Lecture hall AS3, TUAS building

Quantifying social compensation under risks of relationship weakening


Social networks require active relationship maintenance if they are to be kept at a constant level of emotional closeness. For group-living animals, including humans, failure to interact leads inexorably to a decline in relationship quality, and a consequent loss of the benefits that derive from individual relationships. As a result, many social species compensate for weakened relationships by investing more heavily in them. Here we study how humans behave in similar situations, using data from mobile call detail records from a European country. For the less frequent contacts between pairs of communicating individuals we observe a logarithmic dependence of the duration of the succeeding call on the time gap with the previous call. We find that such behaviour is likely when the individuals in these dyadic pairs have the same gender and are in the same age bracket as well as being geographically distant. Our results indicate that these pairs deliberately invest more time in communication so as to reinforce their social bonding and prevent their relationships decaying when these are threatened by lack of interaction.


M. Nelimarkka - Asking societal questions – a take from political scientist

Matti Nelimarkka (HIIT)

Monday 2016-05-30 12.00 - 13.00

Lecture room AS3, TUAS

Asking societal questions – a take from political scientist


It seems that computational social science is done to demonstrate skills in computation. What kind of collaboration might be needed to improve the situation and what are the challenges in such collaborations? I will reflect the challenges from Digivaalit 2015 and other computational social science projects done together with University of Helsinki, Faculty of Social Science.


C. Purcell - Necessary conditions for efficient role colouring

Christopher Purcell (Aalto University)

Monday 2016-05-16 11.00 - 12.00

Lecture room AS3, TUAS building

Necessary conditions for efficient role colouring


A role colouring of a graph is an assignment of colours to its vertices such that two vertices having the same colour have the same set of colours in their respective neighbourhoods. In a network, nodes with similar roles may have similar neighbourhoods in one way or another. A role colouring is an attempt to formalise an aspect of this phenomenon. This talk will focus on the computational complexity of finding such a colouring with a given number of colours. In particular, we have a sequence of infinitely narrowing classes of graphs for which the problem remains NP-hard, and therefore a necessary condition for a polynomial-time solution in a restricted graph class. Joint work with Puck Rombach.

This seminar is organized jointly with the Large Structures seminar.


Opinions and hierarchies: an agent based model

Jan Snellman (Aalto University)

Monday 2016-05-09 11.00 - 12.00

Lecture hall AS3

Opinions and hierarchies: an agent based model


Will to compete, striving for better status and envy are well known human characteristics. Human societies, on the other hand, are hierarchical by character. The purpose of this research project is to study, whether one can mimic the behaviour of both human societies and individuals in agent based models if one assumes that the main motivation of humans is to maximize their status relative to other humans.


O. Korhonen - How consistent are ROIs as nodes of functional brain networks?

Onerva Korhonen (Aalto University)

Monday 2016-05-02 11.00 - 12.00

Seminar room 1021-1022, TUAS building

How consistent are ROIs as nodes of functional brain networks?


The functional network approach, where fMRI BOLD time series are mapped to complex networks depicting functional relationships between brain areas, has opened new insights into the structure and function of the human brain. In this approach, choosing what the network nodes represent is of crucial importance. One option is to consider individual fMRI voxels as nodes. This results in a large number of nodes and may make network analysis challenging, not only in terms of computational cost but also in terms of interpretation of results. A common alternative is to build the nodes of functional brain networks from pre-defined clusters of anatomically close voxels, known as Regions of Interest (ROIs). This approach assumes that voxels within ROIs are functionally similar and have reasonably similar dynamics, which may not always be true. Because these two approaches are known to result in dissimilar network structures, we argue that it is crucial to understand what happens to network connectivity when moving from the voxel level to the ROI level. We study the consistency of ROIs, defined as the mean Pearson correlation coefficient between the time series of voxels within a ROI, and show that this consistency varies widely in resting-state experimental data. This indicates that the underlying assumption of similar voxel dynamics within each ROI may not generally hold. We then show that this variation is reflected in network properties: consistency correlates with node connectivity and link weight in ROI-level networks.


Journal club - Mastering the game of Go with deep neural networks and tree search

April 25, 2016 @ 11.00 - 12.00
Lecture room AS3, TUAS
Presenter: Pietro Della Briotta Parolo

Authors: David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

Download the article here
See also this news item on the article.


Journal club - Quantifying the Diaspora of Knowledge in the Last Century

April 18, 2016 @ 11.00 - 12.00
Lecture room AS3, TUAS
Presenter: Mikko Kivelä

Authors: Manlio De Domenico, Elisa Omodei, Alex Arenas
Academic research is driven by several factors causing different disciplines to act as “sources” or “sinks” of knowledge. However, how the flow of authors’ research interests – a proxy of human knowledge – evolved across time is still poorly understood. Here, we build a comprehensive map of such flows across one century, revealing fundamental periods in the raise of interest in areas of human knowledge. We identify and quantify the most attractive topics over time, when a relatively significant number of researchers moved from their original area to another one, causing what we call a “diaspora of the knowledge” towards sinks of scientific interest, and we relate these points to crucial historical and political events. Noticeably, only a few areas – like Medicine, Physics or Chemistry – mainly act as sources of the diaspora, whereas areas like Material Science, Chemical Engineering, Neuroscience, Immunology and Microbiology or Environmental Science behave like sinks.

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Sitompul Taufik - Aalto Data Management: Evaluation of EUDAT Services for Aalto Data Repository

Sitompul Taufik (Aalto University)

Monday 2016-04-11 11.00 - 12.00

Lecture hall AS3

Evaluation of EUDAT Services for Aalto Data Repository


As one of initiatives to encourage open access publishing, Aalto University has defined its Research Data Management Policy, which aiming to make data management easier for the individual researcher and opening publicly funded scientific research data to achieve wide societal impact and the strategic goals of Aalto University. In order to achieve this objective, Aalto University considers EUDAT services (www.eudat.eu) would probably be the most suitable tool for addressing Aalto research data management requirements. The usability testing will be conducted and participation as pilot users will be required. The result of these activities will determine which platform that will be used for research data management within Aalto University.


Darko Hric - Network structure, metadata and the prediction of missing nodes

Darko Hric (Aalto University)

Monday 2016-04-04 11.00 - 12.00

Location: Lecture hall AS3

Network structure, metadata and the prediction of missing nodes

The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure. Most often, the suitability of the annotations as topological descriptors itself is not assessed, and hence ultimately it is not possible to distinguish between actual shortcomings of the community detection algorithms on one hand, and the incompleteness, inaccuracy or structured nature of the data annotations themselves on the other. In this work we present a principled method to access both aspects simultaneously. We construct a joint generative model for the data and metadata, and a non-parametric Bayesian framework to infer its parameters from annotated datasets. We assess the quality of the metadata not according to its direct alignment with the data groups, but rather in its capacity to predict the placement of nodes in the network. We show how this feature can be used to predict the connections to missing nodes when only the metadata is available. By investigating a wide range of datasets, we show that while there are seldom exact correspondences between metadata tokens and the inferred data groups, the metadata is often informative of the network structure nevertheless, and can improve the prediction of missing nodes. This shows that the method uncovers meaningful patterns in both the data and metadata, without expecting or requiring a perfect agreement between the two.

The preprint in on arXiv: 1604.00255


Ilkka Kivimäki - Functionality of geographic landscapes and randomized shortest paths

Ilkka Kivimäki (Aalto University)

Monday 2016-03-14 11.00 - 12.00

Lecture hall AS3

Functionality of geographic landscapes and randomized shortest paths

I will present the current state in our development of a method for assessing the functionality of geographic landscapes for wildlife. The method aims at combining the consideration of habitat quality an connectivity into a consistent metric that can be used for assessing the impact of local changes on the whole landscape. In particular, we study the impact of construction of hydropower infrastructure on the habitat of wild reindeer populations in Norway. Network measures have been used in ecological literature for such problems, but they are often either based on shortest, i.e. least-cost paths, or on current flow, i.e. random walks. We will extend these ideas in hope of increased realism by considering randomized shortest paths (RSPs) that can be used to derive generalizations of classical network measures. RSPs have also been previously shown suitable for modelling the migration patterns of wild reindeer, when compared with data collected from individual animals equipped with GPS collars [1]. The current proposal for habitat functionality is an adjusted, weighted version of the harmonic centrality with the RSP-based free energy distance."

[1] Panzacchi, Manuela, et al. "Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths." Journal of Animal Ecology 85.1 (2016): 32-42.


Shuhei Miyano - Group Formation through Indirect Reciprocity

Shuhei Miyano (University of Tokyo)

Monday 2016-02-29 11.00 - 12.00

Lecture hall AS3

Group Formation through Indirect Reciprocity

In-group favoritism is the tendency that people are generally more cooperative to the members of the same group.
To understand in-group favoritism, one can assume given group structure and how cooperative relationship is sustained only inside of the groups [1].
In some case, however, the group structure can be formed spontaneously by social interaction of people.
In this talk, I will talk about the spontaneous group formation through cooperative interaction in indirect reciprocity, which is one of the hypothesis explaining how cooperative relationship can be formed and be sustained [2].

[1] N. Masuda, J. Theor. Biol. 311, 8 (2012).
[2] M. A. Nowak and K. Sigmund, Nature 393, 573 (1998).


C. Weckström - Transit-oriented Development in Helsinki Capital Region

Christoffer Weckström (Aalto University)

Monday 2016-02-15 11.00 - 12.00

AS3, Tuas building

Transit-oriented Development in Helsinki Capital Region

The thesis tries to show how land use shapes travel choices in Helsinki Capital region and how land uses producing different travel behavior outcomes are located within the study area. The literature review covers the theory behind how land use and travel choices are linked as well as previous models used in TOD evaluation. A logistic regression model establishes the relationship between land use features and mode choice. In addition, the railway and metro station vicinities are evaluated and categorized based on the Transit-oriented Development (TOD) concept. A TOD index derived from the mode choice model, was used as a tool in this categorization.


Garimella Kiran - Quantifying Controversy on Social Media

Garimella Kiran (Aalto University)

Monday 2016-03-07 11.00 - 12.00

Seminar room 1021-1022, TUAS building

Quantifying Controversy on Social Media

Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content.

Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph.

We perform an extensive comparison of controversy measures, as well as graph building approaches and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.


J. Cambe - Modeling social tie formation and neighborhood turnover

Jordan Cambe (ENS Lyon)

Monday 2016-03-21 11.00 - 12.00

Lecture hall AS3

Modeling social tie formation and neighborhood turnover

During this presentation, I will introduce ways to model tie formation dynamics in time-dependent networks. More precisely, starting from the activity driven model, which is an agent-based model only assuming heterogeneous activities of agents to create links, we will then extend it successively to integrate social mechanisms empirically observed. A first extend will be the addition of a reinforcement process to take into account agents' memory. Finally the 2nd extend will be the addition of an edge removal process, in order to describe the social circle renewal. After having presented these models we will look at how these extends modify the behavior of spreading process.


Enrico Glerean - Brain networks: current state and future challenges

Enrico Glerean (Aalto University)

Monday 2016-02-22 11.00 - 12.00

Lecture hall AS3

Brain networks: current state and future challenges

In this talk I will briefly discuss the current state of mapping brain connectivity in animals and humans. I will then focus on the study of human brain networks and set out a series of open challenges for the network scientists. Specifically, novel methods are needed to operate across 5 dimensions:

1) space (increasingly larger networks due to higher quality of data)
2) individual variability (individual subjects with individual phenotypical/behavioral/genetic/clinical variables versus group averages)
3) imaging modalities (integration of multiple modalities/types of data such as structural MRI, functional MRI, M/EEG, PET, DTI/DSI, modeling, etc)
4) time (networks evolving in time at ms/seconds/hours/years temporal scales)
5) networks of networks (two-persons neuroscience)

Presentation slides


T. Alakörkkö - Effect of spatial smoothing on functional brain networks

Tuomas Alakörkkö (Aalto University)

Monday 2015-02-08 11.00 - 12.00

Lecture hall AS3

Effect of spatial smoothing on functional brain networks

Network analysis of fMRI data has become a popular method in the study of human brain function. Building the network has many problems like how to define nodes and edges. Anatomical and functional parcellations are often used to define nodes and Pearson correlation coefficient of the time series is used to define edges between nodes in most the studies. Hubs are nodes that are considered to be important regions for information processing in the brain and they are most often detected by degree, clustering coefficient, betweenness centrality, eigenvector centrality and closeness centrality of the node.
Spatial smoothing is one of the common preprocessing steps of fMRI data but its usage in network analysis has not been properly validated. Smoothing increases correlations of neighboring voxels. If the two voxels are in different regions of interest (ROIs), correlation between the signals of these two regions is increased. This is problematic because it changes the network structure and therefore reduces the reproducibility of the results.


Journal club - Modeling sequences and temporal networks with dynamic community structures

February 1, 2016 @ 11.00 - 12.00
Lecture room AS3, TUAS
Presenter: Mikko Kivelä

Authors: Tiago P. Peixoto, Martin Rosvall
Community-detection methods that describe large-scale patterns in the dynamics on and of networks suffer from effects of limited memory and arbitrary time binning. We develop a variable-order Markov chain model that generalizes the stochastic block model for discrete time-series as well as temporal networks. The temporal model does not use time binning but takes full advantage of the time-ordering of the tokens or edges. When the edge ordering is random, we recover the traditional static block model as a special case. Based on statistical evidence and without overfitting, we show how a Bayesian formulation of the model allows us to select the most appropriate Markov order and number of communities.

Download the article here


Journal club - Returners and explorers dichotomy in human mobility

January 25, 2016 @ 11.00 - 12.00
Room 1593, TUAS
Presenter: Rainer Kujala

Authors: L. Pappalardo, F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti, A.-L. Barabási
The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.

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Journal club - Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity

January 18, 2016 @ 11.00 - 12.00
Room AS3, TUAS
Presenter: Onerva Korhonen

Authors: Richard F. Betzel, Alessandra Griffa, Andrea Avena-Koenigsberger, Joaquín Goñi, Jean-Phillippe Thiran, Patric Hagmann, Olaf Sporns

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. Past studies have often used single-scale modularity measures in order to infer the connectome's community structure, possibly overlooking interesting structure at other organizational scales. In this report, we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of influence-spreading and diffusion, and brain function. It further suggests that the spread of influence among brain regions may not be limited to a single characteristic scale.

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Davide Cellai - Multiplex networks: models in telecommunications and infrastructures

Dr. Davide Cellai (University of Limerick)

Thursday 2015-01-14 11.00 - 12.00

Room 1021-1022

Multiplex networks: models in telecommunications and infrastructures

Many human-made and social systems in the present world can be described as multiplex networks, i.e. as a set of networks coupled to each other. In this talk, we will see how a multiplex approach can give useful insights into telecommunication and infrastructures. First, I will talk about telecommunication churn (company subscribers who leave a service) and how distinguishing different layers of engagements between subscribers can improve churn prediction. Regarding infrastructures, it has been shown that, for example in transportation networks, there may be a relatively small fraction of nodes that allow the communication between layers. Therefore, I will briefly present two mathematical models aimed to deal with this problem. First, we will introduce a new concept of percolation, where nodes need to be in the largest component only in the layers where they have non-zero degree, and explore how this notion changes with respect to mutual percolation. Finally, we will see the effect of classical bond percolation on multiplex networks with small coupling between layers. This topology can naturally generate multiple percolation transitions that may be relevant in transportation systems.