On Tuesday, April 19, IU School of Informatics and Computing hosted its Spring Research Symposium, where NaN was represented by two undergraduate research projects mentored by PhD candidate Clayton A Davis. Keychul Chung received 2nd prize honors for his work on a browser-based tool to compare historical trends of Twitter hashtag use. Kibeom Alex Hong presented a web-based tool to visualize geospatial trends in Twitter hashtag distribution over time. Both projects will be available as part of the Social Media Observatory tools to be released in early May.
Network science has allowed us to understand the organization of complex systems across disciplines. However, there is a need to understand how to control them; for example, to identify strategies to revert a diseased cell to a healthy state in cancer treatment. Recent work in the field—based on linear control theory—suggests that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics. Such graph-based approaches have been used, for instance, to suggest that biological systems are harder to control and have appreciably different control profiles than social or technological systems. The methodology has also been increasingly used in many applications from financial to biochemical networks.
In work published today in Nature Scientific Reports, CNetS graduate student Alexander Gates and Professor Luis Rocha demonstrate that such graph-based methods fail to characterize controllability when dynamics are introduced. The study computed the control profiles of large ensembles of multivariate systems as well as existing Systems Biology models of biochemical regulation in various organisms.
Congratulations to Clayton Davis, who won the best presenter prize at WWW 2016 Developers Day! Clayton presented BotOrNot: A system to evaluate social bots, a paper coauthored with Onur Varol, Emilio Ferrara, Alessandro Flammini and Filippo Menczer, that describes our latest API developments with the BotOrNot system.
The Center for Complex Networks and Systems Research (CNetS.indiana.edu), jointly with the Indiana University Network Science Institute (IUNI.iu.edu), has
two three open postdoctoral positions, two on the characterization and modeling of complex systems and one to study critical processes in networks of networks. The appointments start in Summer/Fall 2016 for one year and are renewable for one or two additional years, subject to funding and performance. The salary is competitive and benefits are generous.
The postdocs will join a dynamic and interdisciplinary team that includes computer, physical, and cognitive scientists. Two postdocs will work with Prof. Santo Fortunato on various areas of complex systems research, including community detection in networks, computational social science (opinion dynamics, online experiments on social influence) and science of science (citation and collaboration patterns between scientists, impact dynamics). A third postdoc will work with Prof. Filippo Radicchi. Continue reading Three postdoc positions in complex networks and systems
In an interview aired on the ABC (Australian) evening news program “The World” on April 4, 2016, Filippo Menczer discussed with host Beverley O’Connor how information and misinformation spread throughout the Internet and the roles of network structure and social bubbles in determining meme virality. Video here.
Recent CASCI Complex Systems & Networks Phd program graduate Artemy Kolchinsky, is now a postdoc at the Santa Fe Institute. While at SFI, Kolchinsky is working with “David Wolpert on several projects related to optimal use of information and prediction. One is the problem of modeling and analyzing complicated dynamical systems that require large amounts of time and computational power to simulate. […] Another project investigates connections
between information processing and statistical physics. […] The two are [also] beginning to work on understanding why different social groups develop different organizations, whether the group is a prehistoric tribe or a business firm.” More details on the SFI update newsletter.
Congratulations to Luis Rocha, who has been awarded a Fulbright Scholarship devoted to developing Complex Systems methodologies for the Life Sciences. The 12-month sabbatical is to be pursued under the Fulbright program for educational exchanges between the United States and Portugal. It will focus on studying collective behavior and control in biochemical and social networks. The broader goal is to advance our ability to predict and control the dynamics of complex networks in three domain areas: biochemical, neurodyamic, and social systems. The scholarship will also be used to facilitate the design of a new doctoral program on complex networks and systems for the life sciences.
Congratulations to Filippo Radicchi, who has been awarded a Faculty Early Career Development (CAREER) grant from the National Science Foundation to establish a research and education program devoted to studying critical infrastructures from the perspective of network theory. The $500,000 grant will focus on how physical networks, such as transportation, water, food supply, communications, and power generation and transmission, interact to deliver their assets as efficiently as possible. Transactional and relational infrastructures, such as financial and trade networks, also enter into the equation to serve as the backbone for modern society. Read more…
Update: On March 21st, 2016 the paper described below (PMC4720984) was highlighted by Russ Altman from Stanford University in his yearly review as one of 30 important papers of the year in translational bioinformatics.
Using complex networks analysis and social media mining, CNETS researchers from the CASCI team have found that Instagram, a growing social media platform among teens, can be used “to uncover drug-drug interactions (DDI) and adverse drug reactions (ADR).” The work shows that this popular social media service is “a very powerful source of data with great promise in the public-health domain”. The study, “Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines,” supported by an R01 grant from the National Institutes of Health as well as a gift from Persistent Inc., was recently published and presented at the Pacific Symposium on Biocomputing (PSB 2016), in Hawaii. (PubMed, arXiv). The results are based on almost 7.000 user timelines associated with depression drugs which combined have 5+ million posts.
Modularity in complex systems can be observed in networks and across dynamical states, time scales, and in response to different kinds of perturbations. In a paper published in Physical Review E (Rapid Communication), Kolchinsky, Gates & Rocha propose a principled alternative to detecting communities in static and dynamical networks. The method demonstrates that standard modularity measures on static networks can be seen as a special case of measuring the spread of perturbations in dynamical systems. Thus, the new method offers a powerful tool for exploring the modular organization of complex dynamical systems.