The Center for Complex Networks and Systems Research (CNetS) is part of the School of Informatics and Computing and the Pervasive Technology Institute of Indiana University. The center supports and enhances the research efforts of the complex systems group, which has been active within the School since 2004. CNetS is meant to foster interdisciplinary research in all areas related to complex networks and systems. On this website you can find information on CNetS faculty, research groups, and their activities.
Each year since 2005, Microsoft Research has awarded Microsoft Research Faculty Fellowships to promising, early-career academics who are engaged in innovative computing research and have the potential to make significant advances in the state of the art. These fellowships—which include a cash award and access to software, invitations to conferences, and engagements with Microsoft Research—allow bright young academics to devote their time to pushing the boundaries of computer science research, freed from the distraction of grinding out grant proposals. The 2014 Faculty Fellows are seven young professors from around the world who are exploring groundbreaking, high-impact research—the kind of research that has the potential to solve seemingly intractable societal problems.
Among them is our own YY Ahn, who was recognized for his mathematical and computational methods to study complex systems, such as cells, the brain, society, and culture. His recent contribution includes a new framework to identify pervasively overlapping modules in networks, network-based algorithms to predict viral memes, and a new computational approach to study food culture. Congratulations to YY for this highly prestigious award!
We are excited to announce that the ACM Web Science 2014 Conference will be hosted by our center on the beautiful IUB campus June 23–26, 2014. Web Science studies the vast information network of people, communities, organizations, applications, and policies that shape and are shaped by the Web, the largest artifact constructed by humans in history. Computing, physical, and social sciences come together, complementing each other in understanding how the Web affects our interactions and behaviors. Previous editions of the conference were held in Athens, Raleigh, Koblenz, Evanston, and Paris. The conference is organized on behalf of the Web Science Trust by general co-chairs Fil Menczer, Jim Hendler, and Bill Dutton. Follow us on Twitter and see you in Bloomington!
The DESPIC team at the Center for Complex Systems and Networks Research (CNetS) presented a demo of a new tool named BotOrNot at a DoD meeting held in Arlington, Virginia on April 23-25, 2014. BotOrNot (truthy.indiana.edu/botornot) is a tool to automatically detect whether a given Twitter user is a social bot or a human. Trained on Twitter bots collected by our lab and the infolab at Texas A&M University, BotOrNot analyzes over a thousand features from the user’s friendship network, content, and temporal information in real time and estimates the degree to which the account may be a bot. In addition to the demo, the DESPIC team (including colleagues at the University of Michigan) presented several posters on Scalable Architecture for Social Media Observatory, Meme Clustering in Streaming Data, Persuasion Detection in Social Streams, High-Resolution Anomaly Detection in Social Streams, and Early Detection and Analysis of Rumors. See more coverage of BotOrNot on PCWorld, IDS, BBC, and Politico.
On August 11, 2013, the New York Times published an article by Ian Urbina with the headline: I Flirt and Tweet. Follow Me at #Socialbot. The article reports on how socialbots (software simulating people on social media) are being designed to sway elections, to influence the stock market, even to flirt with people and one another. Fil Menczer is quoted: “Bots are getting smarter and easier to create, and people are more susceptible to being fooled by them because we’re more inundated with information.” The article also mentions the Truthy project and some of our 2010 findings on political astroturf.
Inspired by this, the writers of The Good Wife consulted with us on an episode in which the main character finds that a social news site is using a socialbot to bring traffic to the site, defaming her client. The episode aired on November 24, 2013, on CBS (Season 5 Episode 9, “Whack-a-Mole”). Good show!
A story in Nature discusses a recent paper (preprint) from CNetS members Jasleen Kaur, Filippo Radicchi and Fil Menczer on the universality of scholarly impact metrics. In the paper, we present a method to quantify the disciplinary bias of any scholarly impact metric. We use the method to evaluate a number of established scholarly impact metrics. We also introduce a simple universal metric that allows to compare the impact of scholars across scientific disciplines. Mohsen JafariAsbagh integrated this metric into Scholarometer, a crowdsourcing system developed by our group to collect and share scholarly impact data. The Nature story highlight how one can use normalized impact metrics to rank all scholars, as illustrated in the widget shown here.
Findings by CNetS researchers on social media indicators of election results received significant coverage in the national press. The paper More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior by Joseph Digrazia, Karissa McKelvey, Johan Bollen, and Fabio Rojas was presented at the 2013 Meeting of the American Sociological Association in NYC. It was covered by NPR, The Wall Street Journal, MSNBC, C-SPAN, The Washington Post, The Atlantic, and many other media.
Congratulations to Przemyslaw Grabowicz, Luca Aiello, and Fil Menczer for winning the WICI Data Challenge. A prize of $10,000 CAD accompanies this award from the Waterloo Institute for Complexity and Innovation at the University of Waterloo. The Challenge called for tools and methods that improve the exploration, analysis, and visualization of complex-systems data. The winning entry, titled Fast visualization of relevant portions of large dynamic networks, is an algorithm that selects subsets of nodes and edges that best represent an evolving graph and visualizes it either by creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is deployed in the movie generation tool of the Truthy system, which allows users to create, in near-real time, YouTube videos that illustrate the spread and co-occurrence of memes on Twitter. Przemek and Luca worked on this project while visiting CNetS in 2011 and collaborating with the Truthy team. Bravo!