All posts by cnets

CNetS in Science

March 7, 2018Newsscience of sciencecnets

The manifesto of science of science has been published in Science magazine. This is a team effort involving 14 coauthors, three of whom are members of our center: Santo Fortunato (first and corresponding author), Staša Milojević and Filippo Radicchi. The team includes superstars in the field of complex systems like Albert-László Barabási, Dirk Helbing, Alessandro Vespignani, and Brian Uzzi. The paper is a review of the main research topics within science of science: knowledge networks, problem selection, novelty, career dynamics, team science and citation dynamics.

Talk by Giovanni Petri

May 13, 2014special_talktalkscnets

Speaker: Giovanni Petri, Institute for Scientific Interchange (ISI)
Title: Computational Topology for Complex Networks
Date: 05/15/2014
Time: 1pm
Room: Informatics West 107
Abstract: Topological methods for data analysis have recently attracted large attention due to their capacity to capture mesoscopic features which are lost under standard network technique.
In the first part of this talk I will present an application of a recent method from computational topology, persistent homology, to the characterization of spatial patterns in a mobile phone activity of Milan, with particular reference to national communities within the city’s fabric.
We then show how persistent homology can be extended to the case of weighted networks and present a particular application to fMRI data of patients in different states of consciousness.

DESPIC team presents Bot Or Not demo and six posters at DoD meeting

April 25, 2014Fil, NaN, Newsbots, clustering, data mining, detection, information networks, machine learning, rumors, social, Truthy, Twittercnets

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, Politico, and MIT Technology Review.