The National Science Foundation has awarded a $1.9 million grant through the new AccelNet program to the Indiana University Network Science Institute (IUNI), to build an international exchange program focused on multilayer networks. Santo Fortunato, CNetS member and IUNI Director, is the PI of this award, jointly with Alessandro Vespignani, representing the Northeastern University Network Science Institute (NetSI). The project, AccelNet-MultiNet, will establish strong collaborations with scientists of four European institutions: the University of Barcelona in Spain, the ISI Foundation in Turin, Italy, the Central European University in Budapest/Vienna, and the CNRS in Marseille, France. Over the course of five years, 20 researchers from IU and Northeastern University, mostly graduate students, will spend a semester in one of the partner institutions in Europe, and 20 researchers from those institutions will do the same in the U.S. They will work on projects of common interest within the scope of multilayer network science. Read more …
The Indiana University Network Science Institute (IUNI) will be the main organizer of Networks 2021, the largest ever conference in the science of networks. This historical event will be hosted at the Hyatt Regency Washington in Capitol Hill, in Washington DC, on July 6-11, 2021. It will combine the annual meeting of the International Network for Social Network Analysis (Sunbelt XLI), and the annual meeting of the Network Science Society (NetSci 2021). CNetS faculty Santo Fortunato will be one of the two chairs of the conference. Other CNetS faculty will be also actively involved in the organization. Save the date for this great event!
The outcome of the DREAM Challenge on Disease Module Identification in genetic networks has been reported in a paper published in Nature Methods. Over 400 participants from all around the world have contributed 75 different clustering algorithms to predict disease-relevant modules in diverse gene and protein networks. Participants could only use unsupervised clustering algorithms, which rely exclusively on the network structure and do not depend on additional biological information such as known disease genes. CNetS professor Santo Fortunato and former postdoc Lucas Jeub participated in the analysis of the results delivered by the algorithms.Continue reading DREAM Challenge paper published in Nature Methods
Indiana University will establish a $6 million research center to study the role of media and technology in society. With leadership by CNetS faculty, the Observatory on Social Media will investigate how information and misinformation spread online. It will also provide students, journalists and citizens with resources, data and training to identify and counter attempts to intentionally manipulate public opinion. Major support for the center comes from the John S. and James L. Knight Foundation, which will contribute $3 million, as well as funds from the university. The center is a collaboration between the IU School of Informatics, Computing and Engineering, The Media School and the IU Network Science Institute. More…
A team of CNetS researchers has created the first global map of labor flow in collaboration with the world’s largest professional social network, LinkedIn. The work is reported in the journal Nature Communications. The study’s lead authors are Jaehyuk Park and Ian Wood, PhD students working with YY Ahn. Wood is currently a software engineer at LinkedIn. Other authors on the study are CNetS PhD student Elise Jing; Azadeh Nematzadeh of S&P Global, who contributed to the study as a CNetS PhD student; Souvik Ghosh of LinkedIn; and Michael Conover, a CNetS PhD graduate and senior data scientist at LinkedIn at the time of the study. CNetS researchers created the map using LinkedIn’s data on 500 million people between 1990 and 2015, including about 130 million job transitions between more than 4 million companies. The researchers gained access to this data as one of only two teams — IU and MIT — selected to continue their work on the LinkedIn Economic Graph Research program beyond 2017. The study’s result represents a powerful tool for understanding the flow of people between industries and regions in the U.S. and beyond. It could also help policymakers better understand how to address critical skill gaps in the labor market or connect workers with new opportunities in nearby communities. More…
Two CNetS teams were awarded prestigious awards from Minerva, a research initiative of the Department of Defense that supports basic social science research focusing on topics of particular relevance to U.S. national security. One of the two awards will develop Science Genome, a new quantitative framework to investigate science of science using representation learning and graph embedding. The $4.4M project will take advantage of the availability of digitized bibliographic data sets and powerful computational methods, such as machine learning with deep neural networks, to tap into hidden information present in complex scholarly graphs. The project is led by YY Ahn and also includes Staša Milojević, Alessandro Flammini, and Fil Menczer (more…). The other award aims to understand the fundamental laws ruling science dynamics: the description and prediction of the evolution of scientific fields, how to define and measure the novelty of a scientific work, how to assemble successful teams to solve a specific task, and how to define and measure the impact of scholars’ research. The $5M project is led by a consortium of seven prominent science of science experts in four US institutions, including CNetS professor Santo Fortunato (more…). Both projects have potential applications in policy-making, for institutions and funding agencies.
UPDATE: This paper is ranked #3 most read among all articles published by Nature Communications in 2018
Analysis by CNetS researchers of information shared on Twitter during the 2016 U.S. presidential election has found that social bots played a disproportionate role in spreading misinformation online. The study, published in the journal Nature Communications, analyzed 14 million messages and 400,000 articles shared on Twitter between May 2016 and March 2017 — a period that spans the end of the 2016 presidential primaries and the presidential inauguration on Jan. 20, 2017. Among the findings: A mere 6 percent of Twitter accounts that the study identified as bots were enough to spread 31 percent of the low-credibility information on the network. These accounts were also responsible for 34 percent of all articles shared from low-credibility sources. The study also found that bots played a major role promoting low-credibility content in the first few moments before a story goes viral. Continue reading Twitter bots play disproportionate role spreading misinformation
Congratulations to Clayton A. Davis, who successfully defended his PhD dissertation titled “Collect, Count, and Compare”: Expanding Access and Scope of Social Media Analysis. Dr. Davis’ work explored ways to facilitate research using massive social data through tools that are friendly for non-technical users, robust to manipulation by social bots, and that offer strict anonymity guarantees. His work has been featured on the cover of Communications of the ACM and quoted in top worldwide media venues. Web interfaces for his projects, including Botometer, Kinsey Reporter, and the Observatory on Social Media, have served millions of queries to thousands of Internet users. Davis has also made key pedagogical contributions, and co-authored a textbook on network science to be published later this year by Cambridge University Press.
Congratulations to Rion Correia, who successfully defended his PhD dissertation on Prediction of Drug Interaction and Adverse Reactions, with data from Electronic Health Records, Clinical Reporting, Scientific Literature, and Social Media, using Complexity Science Methods. Dr. Correia’s research used network science, machine learning, and data science to uncover population-level associations of drugs and symptoms, useful for public health surveillance. His findings show that Social Media (Instagram and Twitter) and Electronic Health Records of an entire city in Southern Brazil, are very useful to reveal how the Drug interaction phenomenon varies across distinct groups. For instance, he identifying gender biases and specific communities of interest in chronic disease (e.g. Epilepsy and Depression). In addition to Complex Networks and Systems, his dissertation contributes to the fields of biomedical informatics and precision public health by leveraging heterogeneous data sources at multiple levels to understand population and individual pharmacology differences and other public health problems.
Congratulations to Dimitar Nikolov, who successfully defended his PhD dissertation on Information Exposure Biases in Online Behaviors. Dr. Nikolov’s research explored the unintentional biases introduced by filtering, ranking, and recommendation algorithms that mediate our online consumption of information. His findings show that our reliance on modern online technologies limits exposure to diverse points of view and makes us vulnerable to misinformation. In particular, he analyzed two massive Web traffic datasets to quantify the popularity and homogeneity bias of several popular online platforms including social media, email, personalized news, and search engines. He also leveraged Twitter data to characterize the link between political partisanship and vulnerability to online pollution, such as fake news, conspiracy theories, and junk science. His dissertation contributes to the field of computational social science by putting the study of bias in information consumption and derived phenomena like political polarization, echo chambers, and online pollution on a more firm quantitative foundation.