When: Friday, October 4, 2019, 11am

Where: Informatics East, room 322

Speaker: Eun Lee

Homophily and minority-group size explain perception biases in social networks

Abstract:  People’s perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.

Biography: Eun Lee is a post-doctoral researcher in the Department of Mathematics at the University of North Carolina (Chapel Hill) with an interest in contributing to a deeper understanding of the interplay among social network structure, the dynamics on and of that structure, perception, and collective behavior. My research interest lies in four topics:  The effects of the social network structure on perceptions, the effect of perceptions on the collective behavior, the co-evolution of social networks and human behavior, and understanding dynamics on and of temporal and social networks.

When: Tuesday, May 7, 2019, 2:00 pm

Where: Informatics West, Room 232

Speaker: Jinhyuk Yun

Inequality in the formation of collaborative knowledge – the case of Wikipedia

See: J. Yun et al., Nature Human Behaviour 3, 155 (2019)

Abstract:  Wikipedia and its sibling projects have served as a representative medium of worldwide knowledge market to share individuals’ knowledge in the information age. It has been commonly believed that such an open-editing communal data set accelerates democratization of knowledge, shifting the possession of knowledge from privileged class to general public. However, recent studies have observed inequality in authority and power distributions among the editors. One essential question is the underlying mechanism behind abiogenesis of such an unexpected authority. Naturally, lacking consideration of data sets other than English Wikipedia, had obscured such communal data set’s genuine nature of editing dynamics. In this study, we propose unbiased framework encompassing every element of Wikimedia projects. Our analysis, using the complete edit history of 267,304,095 articles from the entire 863 Wikimedia projects, reveals universality in growing regardless its category and language. The interplays between number of edits, number of editors, number of articles, and total length of text is characterized by a single set of exponents. Moreover, we observe the rapid increasing of the Gini coefficient, and suggest that this entrenched inequality stems from the nature of such open-editing communal data sets. We introduce a generative model accompanied with short-term and long-term memories, which successfully elucidates the mechanism behind the oligarchy in Wikipedia.

Biography:  Jinhyuk Yun is a senior research scientist in KISTI (Korea Institute of Science and Technology Information).  He worked as a data scientist at the Search Division in Naver Cooperation after receiving his Ph.D. in statistical physics from KAIST in 2016. His research aims to reveal the hidden structure and dynamics of complex systems with the large-scale dataset and mathematical frameworks focusing on the society, culture, media, collective knowledge, and so on.

When: Friday, February 8, 2019, 2:00 pm

Where: Informatics West, Room 232

Speaker: Didier A. Vega-Oliveros

Complex Networks’ Approaches for Analyzing Climate (and Other Spatiotemporal) Data

Abstract:  Complex network theory has helped to identify valuable information in many domains, where systems are complex with non-trivial connections and properties. In this way, understanding how the network structure impacts the dynamics and also how to infer the structure from these dynamics is of paramount importance to the area. In another perspective, real-world time series reflect inherently nonlinear processes determining the underlying system’s structure and dynamics. A vast amount of time-series data come from many fields, including climate, car accidents, crimes, or neurosciences. In this talk, I will focus on how to analyze and represent spatiotemporal systems on networks, discussing some approaches like visibility graphs, time-series correlation networks, among others. Besides, this talk aims to show the great potentials of time series networks to tackling real-world contemporary scientific problems, and more important, identify and discuss some gaps and research challenges in the area.

Biography:  Didier Vega-Oliveros received his PhD in Computer Science in 2017 from University of Sao Paulo, Brazil. He is a collaborative researcher at the National Institute for Space Research – INPE of Brazil, and recently, a post-doc visitor at Indiana University – Bloomington.  His research deals with network sciences with data mining and its applications, in particular, developing algorithms applied to climate systems, social networks, and disasters risk reduction and management.

When: Tuesday, February 5, 2019, 12:30 pm

Where: Informatics West, Room 232

Speaker: Sadamori Kojaku

Multiple Core-Periphery Pairs in Networks

Abstract:  Networks with core-periphery structure comprise core and peripheral nodes, where core nodes are densely interconnected with each other while peripheral nodes are sparsely interconnected with each other. Many empirical networks are shown to be composed of a single group of core nodes and a single group of peripheral nodes, each of which plays distinct roles in the underlying systems, e.g., leaders and followers in social networks. Here we challenge this long-standing observation: a network may be better regarded as a collection of multiple cores and peripheries, as is the case for communities in networks. We show that heterogeneous degree distributions alone explain a single core-periphery structure. Based on this result, we present a novel algorithm to find multiple groups of core-periphery structure in networks that are not explained by heterogeneous degree distributions. We illustrate our algorithm using various empirical networks, including a political blog network, an inter-bank network and a maritime transportation network.

Biography:  Sadamori Kojaku received his PhD in Information Science in 2015 from Hokkaido University, Japan. He moved to the University of Bristol in U.K. as a postdoctoral scholar.  His research deals with network theory and its applications, in particular, developing algorithms applied to social and economic networks.

Joint Talk

When: Thursday, April 27th, 2017, 2:30 pm

Where: Informatics East, Room 322

Speakers: Diego Raphael Amancio and Filipi Nascimento Silva

Part One

Modelling and characterization of information networks (Diego Raphael Amancio)

Complex networks have been used to model a myriad of real systems, including information. In this presentation, I will focus on the application of network science theory in text classification and scienciometry. I will show how the information obtained from the topological structure of networks can assist typical classification tasks such as authorship recognition and sense disambiguation.

Diego Raphael Amancio received his B.S. degree in computer engineering from University of São Paulo (USP, Brazil) in 2009. He also received a Ph.D. degree in Computational Applied Physics from USP in 2013. Since 2014, he is an assistant professor at USP (Computer Science department). His current research focus on the structural analysis of information networks and applications in scienciometry. He also applies network science in pattern recognition methods

Part Two

Information networks: structure, visualization and applications (Filipi Nascimento Silva)

An information network is a particular case of a complex network in which the nodes can represent pieces of information, such as text, articles, ideas or even abstract concepts; while edges represent their relationships, such as similarity, citations, references, dependence, etc. In this presentation, we will focus on ways to understand the complexity of such systems in terms of dimension and symmetry. We will also show how visualizing information networks can provide interesting insights about their underlying characteristics. Concerning network applications in scienciometry, we will present our work on quantifying interdisciplinary of journals and how text mining techniques can be interwoven with network science to build ontologies on a given scientific area.

Filipi Nascimento Silva received his Bachelor, Master and Ph. D. in computational physics from the São Carlos Institute of Physics (University of São Paulo). Currently, holding a post-doc position at the same institute. Have experience with the analysis of complex systems, data visualization, scientific publication, framework development and web development. His research is based on the use of complex network concepts and approaches to tackle studies on data analysis and visualization of real-world systems. Under an interdisciplinary collaborative environment, he was able to develop interesting research in many fields. This includes studies on biological structures (and systems), scientometry, textual analysis and urban networks. He also developed new tools and frameworks for scientific research as well as for entertainment purpose.
 

Speaker: Hye-Jin Youn, Santa Fe Institute & Senior Research Fellow, University of Oxford
Title: Lost and found in translation: the universal structure of human lexical semantics
Date: 04/12/2017
Time: 11am
Room: Informatics East 322

Abstract: How universal is human conceptual structure? The way concepts are organised in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides direct access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterise. Using cross-linguistic dictionaries, we provide here an empirical measure of semantic proximity between concepts and analyse the structure of a network derived from it. Across languages carefully selected from a phylogenetically and geographically stratified sample of genera, translations of words reveal cases where a particular language uses a single polysemous word to express concepts represented by distinct words in another. We use the frequency of polysemy linking two concepts as a measure of their semantic proximity, and represent the pattern of such linkages by a weighted network. This network is highly uneven and fragmented: certain concepts are far more prone to polysemy than others, and there emerge naturally interpretable clusters that are loosely connected to each other. Furthermore, the networks of different language groups exhibit consistent structures, largely independent of geography, environment, and literacy. We therefore conclude the conceptual structure connecting basic vocabulary studied is primarily due to universal features of human cognition and language use.

Bio:Senior Research Fellow, Mathematical Institute, University of Oxford; James Martin Fellow, The Institute for New Economic Thinking at the Oxford Martin School

How do we, humans, understand the world: categorize and accumulate our knowledge, and thereby innovate idea, culture, and technologies? Is there any universal mechanism that governs such innovation process that eventually generates both social and economic wealth across different societies? A common conceptualization of innovation in both the biological and socio-economic domains sees it as an adaptive process of recombinant search over a space of configurational possibilities. I develop a mathematical framework for recombinant search in the space of configurational possibilities supported strongly by empirical data. The better we understand the mechanism of innovation the better we understand the mechanism of wealth creation.

Research goals: Develop a mathematical framework for economic growth through innovation and tacit knowledge accumulation based strongly on empirical data; Understand universality in the way human categorizes the world, accumulates the knowledge, and innovate new technologies.

Fields: PhD and BA in Statistical Physics at Korea Advanced Institute of Science and Technology; The mathematical Institute at University of Oxford; The Institute for New Economic Thinking; applied mathematics, physics, network theory, urban scaling theory, urban economics, urban geography, knowledge spillover, linguistics, lexical semantics, innovation, science of science, economic growth theory.

Speaker: Alessio Cardillo, École Polytechnique Fédérale de Lausanne
Title: Automatic identification of relevant concepts in scientific publications
Date: 02/10/2017
Time: 12:15pm
Room: Informatics East 322

Abstract: Recently, scientists have devoted many efforts to study the organization and evolution of science by exploiting the textual information contained in the articles like: keywords and terms extracted from title/abstract. However, only few studies focus on the analysis of the core of an article, i.e., its body. The access to the whole text of documents allows to study, instead, the organization of scientific knowledge using networks of similarity between articles based on their whole content.

I use the concepts extracted from the documents/articles available within the ScienceWISE platform to build the network of similarity between them. However, such network possesses a remarkably high link density (36%). As a consequence, attempts of associating groups of documents (communities) to a given topic are of limited success. The reason is that not all the concepts are equally informative and may not be useful to discriminate the articles. The presence of “generic concepts” gives rise to spurious similarities responsible for a large amount of connections in the system.

To get rid of such concepts, I will introduce a method to gauge their relevance according to an information-theoretic approach. The significance of a concept $c$ is encoded by the distance between its maximum entropy, $S_{\max}$, and the observed one, $S_c$. After removing concepts having an entropy within a certain distance from the maximum, I rebuild the similarity network and analyze its community structure (topics). The consequences of this are twofold: the number of links decreases, as well as the noise present in the strength of similarities between articles. Hence, the filtered network displays a more well defined community structure, where each community contains articles related to a specific topic. Finally, the method can be applied to any kind of documents, and works also in a coarse-grained mode since it is able to identify the relevant concepts for a certain set of articles, allowing the study of a documents corpus at different scales.

Bio: Alessio Cardillo is currently postdoc research fellow at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland. His research interests focus on the analysis of the structure of networked systems like: urban mobility and street patterns, scientific collaborations, collections of documents and multiplex networks. He is also interested in the emergence of collective behaviours such as cooperation or synchronization by means of coevolutionary dynamics.

Speaker: Orion Penner, École polytechnique fédérale de Lausanne
Title: The Returns to Scientific Specialization
Date: 11/16/2016
Time: 12:30pm
Room: Informatics East 322

Abstract: While it is well established that researchers specialize, the extent to which they specialize has gone, largely, unexamined. We have developed an approach for measuring the extent to which a researcher is specialized, and in turn, use it to quantify the returns to specialization. In this we exploit a longitudinal dataset 50,000+ researchers, each starting his or her career 1975 or later. Analyzing this dataset we find there are significant returns to specialization. For example, at mean career age and publishing rate, a one-standard deviation increase in specialization leads to a 20 per cent increase in citations. We further show that the returns to specialization are greatest early in a researcher’s career and decrease as a researcher ages. Similarly, returns are greater when publishing at a lower rate and decrease at higher rates of publishing.

Bio: Orion Penner is a Postdoctoral Researcher in the Chair of Innovation and IP Policy at the École polytechnique fédérale de Lausanne. His recent, current and future research largely focuses on the academic career trajectory. Prior to Switzerland, he spent three years at IMT Lucca carrying out research, broadly speaking, on the Economics of Science and Innovation. He earned his PhD in Physics from the University of Calgary, working on problems in complex systems, networks and bioinformatics. He currently holds a Swiss National Science Foundation Ambizione grant, having previously held a SSHRC Postdoctoral Fellowship at IMT Lucca and an NSERC Canada Graduate Scholarship during his PhD.

Speaker: Woo Seong Jo, Sungkyunkwan University
Title: Ph.D. Candidate
Date: 10/12/2016
Time: 11am
Room: Informatics East 322
Abstract: We use user-accessible profiles from a web-based well-known social networking service specialized in business and employment. Users often provide information of their work experiences and positions in firms they worked, and also write what are their work skills. We first construct a bipartite network of users who work or worked at a specific company in 2013 and their skills. After then we make projection to the network of skills. From the time evolution of the skill network constructed for a company, we find that an interesting pattern emerges when the company starts a new business sector. As well as the business strategies, we observe how skills are fused with others in skill network.
Bio: Wooseong Jo is a Ph.D. Candidate in Statistical Physics in Sungkyunkwan University (supervisor: Beom Jun Kim). His interests are in modeling and visualization of complex systems such as society, human dynamics as well as the equilibrium system in statistical physics. He has researched on various subjects: analysis fragility in world-bank networks, dynamics of spreading pests, and traditional problems such as spin system and percolation.

Speaker: Lucas Jeub, Postdoctoral Fellow, School of Informatics & Computing, Indiana University
Title: Local Communities, Mesoscopic Structure, and Multilayer Networks
Date: 09/12/2016
Time: 11:30 am
Room: Informatics East 322
Abstract: There are many methods to detect dense “communities” of nodes in networks,  and there are now several methods to detect communities in multilayer networks. One way to define a community is as a set of nodes that trap a diffusion-like dynamical process (usually a random walk) for a long time. In this view, communities are sets of nodes that create bottlenecks to the spreading of a dynamical process on a network. We analyze the local behavior of different random walks on synthetic and empirical monolayer and multiplex networks (the latter are multilayer networks in which different layers correspond to different types of edges). We show that bottlenecks to random walks can reveal interesting mesoscale structure in networks that go beyond classical communities. There are different ways to generalize a random walk to multilayer networks.  We show that they have very different bottlenecks that hence correspond to rather different notions of what it means for a set of nodes to be a good community. This has direct implications for the behavior of community-detection methods that are based on these random walks. The ill-defined nature of the community-detection problem makes it crucial to develop generative models of networks to use as a common test of community-detection tools. For mono-layer networks different types of benchmark models are available. We develop a family of benchmarks for detecting mesoscale structures in multilayer networks by introducing a generative model that can explicitly incorporate dependency structure between layers. Our benchmark provides a standardized set of null models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. We discuss the parameters and properties of our generative model, and we illustrate its use by comparing a variety of community-detection methods.