Dynamics on and of Complex Networks IX / Mining and learning for complex networks  (DOAO) Session 1

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Time and Date: 10:00 - 12:30 on 20th Sep 2016

Room: N - Graanbeurszaal

Chair: Jean-Charles Delvenne

43000 Modeling sequences and temporal networks with dynamic community structures [abstract]
Abstract: Community-detection methods that describe large-scale patterns in the dynamics on and of networks suffer from effects of arbitrary time scales that need to be imposed a priori. We develop a variable-order hidden Markov chain model that generalizes the stochastic block model for discrete time-series as well as temporal networks. With our model, the relevant time scales are inferred from data, instead of being determined beforehand. The temporal model does not require the aggregation of events into discrete intervals, and instead 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. We formulate an efficient nonparametric Bayesian framework that can select the most appropriate Markov order and number of communities, based solely on statistical evidence and without overfitting.
Tiago Peixoto
43001 Correlation networks from flows [abstract]
Abstract: Complex network theory provides an elegant and powerful framework to statisticallyinvestigate different types of systems such as society, brain or the structure of local andlong-range dynamical interrelationships in the climate system. Network links in correlation,so-called climate networks typically imply information, mass or energy exchange.However, the specific connection between oceanic or atmospheric flows and the climatenetwork?s structure is still unclear. We propose a theoretical approach of flow-networks forverifying relations between the correlation matrix and the flow structure, generalizingprevious studies and overcoming the restriction to stationary flows [1]. We studied acomplex interrelation between the velocity field and the correlation network measures.Our methods are developed for correlations of a scalar quantity (temperature, for example)which satisfies an advection-diffusion dynamics in the presence of forcing and dissipation.Our approach reveals the insensitivity of correlation networks to steady sources and sinksand the profound impact of the signal decay rate on the network topology. We illustrate ourresults with calculations of degree and clustering for a meandering flow resembling ageophysical ocean jet.Moreover, we discuss the follow-up approaches and application of the flow-networksmethod [2].[1] "Correlation networks from flows. The case of forced and time-dependent advectiondiffusiondynamics" L.Tupikina, N.Molkenthin, C.Lopez, E.Hernandes-Garcia, N.Marwan,J.Kurths, Plos One. 2016[2] "A geometric perspective on spatially embedded networks. Quantification of edgeanisotropy and application to flow networks", H.Kutza, N.Molkenthin, L.Tupikina, J.Donges,N.Marwan, U.Feudel, J.Kurths, R.Donner under rev.in Chaos
Lyubov Tupikina
43002 Burstiness and spreading on networks: models and predictions [abstract]
Abstract: When modelling dynamical systems on networks, it is often assumed that the process is Markovian, that is future states depend only upon the present state and not on the sequence of events that preceded it. Examples include diffusion of ideas or diseases on social networks, or synchronisation of interacting dynamical units. In each case, the dynamics is governed by coupled differential equation, where the coupling is defined by the adjacency matrix of the underlying network. The main purpose of this talk is to challenge this Markovian picture. We will argue that non-Markovian models can provide a more realistic picture in the case of temporal networks where edges change in time, or in situations when pathways can be measured empirically. We will focus on the importance of non-Poisson temporal statistics, and show analytically the impact of burstiness on diffusive dynamics, before turning to applications and incorporating memory kernels in predictive models of retweet dynamics.
Renaud Lambiotte
43003 Understanding Markovian Network Dynamics [abstract]
Abstract: In this talk I will talk about approaches for understanding transition behavior on networks. In particular I will first introduce HypTrails - a Bayesian framework for comparing different hypothesesabout human transition behavior in networks. Then I will introduce SubTrails - an approach for identifying subgroups of users with exceptional transition behaviors. I will conclude with open challengesand problems in the field.
Markus Strohmaier
43004 On the degree centrality in multiplex networks - The paper that kills complex network analysis [abstract]
Abstract: In almost any network analytic project, we make multiple, innocent, and seemingly inconsequential modeling decisions - however, some of them might have a profound impact on the results. In this talk, I will discuss how to evaluate the most simple centrality index, the degree, of a node in multiplex networks. Of course, any ranking of all nodes in a multiplex network requires first, some normalization of the degrees to put them on the same scale, and second, some aggregation over the different layers. You might opt for the sum, the average, the mean, the minimum or the maximum as an aggregation strategy. In this talk I will show that the combination of these two strategies, i.e., normalization and aggregation, may result in nodes being identified as either top central or least central. It thus emphasizes the need to properly describe, check, and evaluate even the most simple modeling decisions.
Katharina Zweig
43005 The Effects of Correlation between Influential Level and Threshold in Opinion Dynamics [abstract]
Abstract: In every choice we make, repetitive interactions within our social networks and our susceptibility to influence impact our decisions. This research investigates such mechanisms with the framework of threshold models of social contagion. We find that a strong positive correlation between the degree (representing the strength of influence) and the threshold (displaying acceptance level) slows the dynamics and segregates the opinions.
Petter Holme

Dynamics on and of Complex Networks IX / Mining and learning for complex networks  (DOAO) Session 2

Schedule Top Page

Time and Date: 14:15 - 18:00 on 20th Sep 2016

Room: N - Graanbeurszaal

Chair: Jean-Charles Delvenne

43006 Discrimination in Human vs. Algorithmic Decision Making [abstract]
Abstract: Algorithmic (data-driven) decision making is increasingly being used toassist or replace human decision making in a variety of domains rangingfrom banking (rating user credit) and recruiting (ranking applicants) tojudiciary (profiling criminals) and journalism (recommendingnews-stories). Against this background, in this talk, I will pose andattempt to answer the following high-level questions: (a) Can algorithmic decision making be discriminatory?(b) Can we detect discrimination in decision making?(c) Can we control algorithmic discrimination? i.e., can we make algorithmic decision more fair?
Krishna P. Gummadi
43007 Understanding fashion as a complex network [abstract]
Abstract: Fashion is a very fast moving business addressing many different target groups (style, age, occasion, status, ?). Hence, it is hard to predict what will be fashionable in the future. Originally, how these markets evolved was largely dictated by a relatively small set of players, such as designers, brands or celebrities. Nowadays, social media is to some extend changing the rules of play. Bloggers and other participants in social media are increasingly playing an important role in defining and spreading fashion trends. Zalando wants to take active part in this development. Some of our goals are to discover new influencers in the fashion world, match influencers to specific brands or advertising campaigns and to discover and to monitor emerging trends present in social media. To do this, we have to construct a large network of entities (such as bloggers, brands, magazines) to be able to analyze the dynamic behavior of the fashion world and answer the questions mentioned above. For this talk, we studied a very popular fashion platform with around 1M subscribers and 22M connections. We will address questions such as which geographical regions of the world are most active or if people tend to follow influential users from their same country or region. In addition, we will analyze if some of the standard properties of complex networks apply in our example, such as small world, scale free, etc.
Julien Siebert
43008 Phase Transitions in the Growth of Spatial Networks? [abstract]
Abstract: Spatially embedded complex networks, such as nervous systems, the Internet, and transportation networks, generally have nontrivial topological patterns of connections combined with nearly minimal wiring costs. We report here the empirical analysis of two databases describing respectively: 200 years of evolution of the road network in a large area located north of Milan (Italy), and the growth of the nervous system of the C. elegans from the moment of fertilization to adulthood. We discuss the basic mechanisms that drive the evolution of such two spatial networks.
Vito Latora
43009 Stream Graphs and Link Streams for the Modeling of Interactions Over Time [abstract]
Abstract: The structure and dynamics of interactions is crucial for many phenomena of interest, like contacts between individuals, data transfers, commercial exchanges, mobility, and many others. Analyzing such interactions classicaly relies on network analysis, which captures the structure of interactions, or on temporal series, which captures their dynamics. Both approaches have been extended in various ways to cope with the both structural and temporal nature of interactions, but current situation remains unsatisfactory. I will present here the modeling of interactions over time by stream graphs and link streams, which aims at unifying both aspects into a simple, efficient and intuitive way. It provides a language to deal with interactions over time, in a waysimilar to the language provided by network science for relations.
Matthieu Latapy
43010 Syntactic Complexity of Web Search Queries through the Lenses of Language Models, Networks and Users [abstract]
Abstract: Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication. Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.
Rishiraj Saha Roy