When: November 11, 2022 12pm EST

Where: Remote Talk

Zoom link: https://iu.zoom.us/j/89873992894?pwd=QmxmaXZaakNFSUpnQm1jT1I3T1ZOZz09

Recording: Jonas Juul Cascades Talk

Speaker: Jonas Juul

Title: Harder, better, faster, stronger cascades — or simply larger?

Abstract:

Do some types of online content spread faster or further than others? In recent years, many studies have sought answers to such questions by comparing statistical properties of network paths taken by different kinds of content diffusing online. Here we demonstrate the importance of controlling for correlations in the statistical properties being compared. In particular, we show that previously reported structural differences between diffusion paths of false and true news on Twitter disappear when comparing only cascades of the same size; differences between diffusion paths of images, videos, news, and petitions persist. Paired with a theoretical analysis of diffusion processes, our results suggest that in order to limit the spread of false news it is enough to focus on reducing the mean “infectiousness” of the information.
Joint work with Johan Ugander (Stanford University)

Biography:

Jonas L. Juul is a Carlsberg Fellow postdoc at the Technical University of Denmark. His research focuses on spreading processes and networks. His recent interests include developing methods to infer how content spreads online from observed diffusion paths, and evaluating the efficiency of mitigation measures in epidemiology. Before joining the Technical University of Denmark he was a post-doctoral researcher at the Center for Applied Mathematics, Cornell University where he worked with Austin Benson, Jon Kleinberg and Steven Strogatz. He obtained his Ph.D. in Physics of Complex Systems from the Niels Bohr Institute in 2020.

When: October 15, 2021,  12:00pm

Where: Luddy 1106 (Dorsey Learning Hall)

Zoom link: https://iu.zoom.us/j/89107137699

Speaker: Francesco Pierri 

Title: Characterization and detection of disinformation spreading in online social networks

Abstract:

Online social media expose us to a variety of false and misleading information which erodes public trust towards institutions, with severe backlashes in the real world. One example is the on-going COVID-19 pandemic, as the world experiences an “infodemic”, an overabundance of information including false and misleading content, which undermines medical intervention to fight the disease.

In this talk, I will present results from the research carried out during the last three years as a Ph.D. candidate in Politecnico di Milano, and that will be part of my forthcoming Ph.D. dissertation. I will also present results from my on-going collaboration with the Observatory on Social Media.

I leveraged a computer science and network science approach to tackle the problem of disinformation spreading in online social networks from two perspectives: (1) characterization, i.e., understanding the mechanisms and the actors involved in the spread of false and misleading information on online social media during relevant events such as political elections and the on-going COVID-19 pandemic; (2) detection, i.e., building a methodology to accurately classify news articles based on the interactions between users that take place on platforms like Twitter.

Biography:

I am a 3^rd (last) year Ph.D. student in the “Data Analytics and Decision Sciences” Ph.D. program at Politecnico di Milano, under the supervision of prof. Stefano Ceri and prof. Fabio Pammolli.

The focus of my research (and my Ph.D. dissertation) is to understand the spread of disinformation in online social networks, with both a computer science and network science approach. I also recently investigated the socio-economic consequences of the COVID-19 pandemic by leveraging human mobility data from mobile phones.

I am currently a visiting scholar at the Observatory on Social Media (since September 2020), where we investigate the COVID-19 infodemic and the spread of vaccine-related misinformation.

When: Friday, May 14, 2021

Where: https://iu.zoom.us/j/87346711649

Speaker: Chris Connell

Title: The DynACPD network embedding algorithm for prediction tasks on dynamic networks

Abstract: Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes. Such embeddings are important for tasks such as link prediction and node classification. We consider low dimensional embeddings of “dynamic networks” — a family of time varying networks where there exist both temporal and spatial link relationships between nodes. We present novel embedding methods for a dynamic network based on higher order tensor decompositions for tensorial representations of the dynamic network. Our embeddings are analogous to certain classical spectral embedding methods for static networks. We demonstrate the effectiveness of our approach by comparing our algorithms’ performance on the link prediction task against an array of current baseline methods across three distinct real-world dynamic networks. Finally, we provide a mathematical rationale for this effectiveness in the regime of small incremental changes. This is joint work with Yang Wang.

Biography:

Chris Connell is interested in problems at the interface between dynamical systems, random walks, and the topology and geometry surrounding nonpositive curvature. Some of his recent work emphasizes bringing tools from these disciplines to bear on network embedding problems. He received his PhD from the University of Michigan and is now a professor of mathematics at Indiana University Bloomington.

When: Wednesday, October 2, 2019, 2pm

Where: Informatics East, room 322

Speaker: Jean-Gabriel Young

Efficient and fully Bayesian inference of complex networks from noisy data

Abstract: Rarely do we have access to error-free measurements of networks. Instead, we typically get to observe sequences of states that are, at best, indirect observations of a system’s structure. Recent research has led to a formalization of just how much, and in which ways, these measurements can inform us on the structure of real complex networks. It is now understood that domain-agnostic models are not silver bullets—one can come up with many different models of how a data set maps to a network, all leading to different inferences. The motivation for the work presented here is the realization that this leads to a tension preventing a broad adoption of these network reconstruction methods by practitioners.  On the one hand, domain expertise must necessarily go into devising good models, as to avoid erroneous conclusions. But at the same time, designing models can be challenging because one has to: derive a complete inference procedure from scratch; implement this procedure; verify the inference; and start anew if the results are not correct—for every model.

In this presentation, I will introduce a Bayesian framework that abstracts away most of the computational work, putting flexible model design center stage. The crucial modeling task that our framework leaves to the practitioner is that of determining how individual pair-wise measurements of interaction are explained by the presence or absence of an edge between two nodes. The method is broadly applicable in that these measurements can be anything, from a straightforward number of observed interactions, to time-series, or pairs comprising of a number of attempted and successful observations.

Biography:

Jean-Gabriel Young is broadly interested in problems at the intersection of statistics and complex systems. His recent work focuses on new exciting inference problems in network science, including the inference of the past of dynamical networks, network reconstruction from noisy data; and the inference of high-order interactions from pairwise data. He received his Ph.D. in Physics from Université Laval (2018), and is now a James S. McDonnell postdoctoral Fellow in complexity at the University of Michigan.

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.