James H. Rudy Professor of Informatics and Computing at the Luddy School of Informatics, Computing, and Engineering
Member of the Center for Complex Networks and Systems Research (CNetS)
Indiana University, Bloomington, USA
Fellow of the American Physical Society
Fellow of the Network Science Society
Author of A First Course in Network Science
News
Honored to be named James H. Rudy Professor of Informatics and Computing at Indiana University. Thanks to all my mentors, advisors, students, postdocs, and collaborators for pushing me to this career high. See Luddy's press release here.
I have co-organized the network science program for the 2026 Global Physics Summit of the American Physical Society, held in Denver. Thanks to my co-organizers Raffaella Burioni, Guido Caldarelli and Filippo Radicchi.
I am the top faculty of Luddy School according to Stanford/Elsevier Top 2% Scientists Ranking.
I was named an inaugural Highly Ranked Scholar by ScholarGPS.
I have organized and given A Short First Course in Network Science, a two-weeks online basic course for people interested in the topic, held in June 2024, sponsored by the Network Science Society. You can find the videos of the lectures here.
Our overview of network community detection on the 20th anniversary of the Girvan-Newman paper is out in Nature Physics! Delightful first collaboration with the great Mark Newman
Recent papers
The leading eigenvectors of the adjacency matrix miss the community detectability transition in sparse networks generated by the planted partition model. But the graph energy (or nuclear norm), that uses the full spectrum, can see it! Check out our paper, just out on the arXiv.
Our review article on multilayer network science is out on the arXiv. Thanks to all collaborators and friends of the AccelNet MultiNet project, it was great working with you all!
The Covid pandemic has accelerated an ongoing shift towards remote working. Plus: scientists frequently collaborate with people from other institutions. Minus: paper impact has decreased (are online interactions less creative?). Here is the story.
When you randomize a weighted network, be careful! Results may depend on the unit you use to compute the weights, particularly when you want to know if your results are statistically significant. You find the details here.
When you do your orders online you have to be patient! 5 minutes of extra waiting time would reduce life-cycle CO2 emissions by 20%. Check out our preprint!
Equations used to describe neuronal avalanches in living brains can also be applied to cascades of activity in deep neural networks. Leveraging non-equilibrium statistical physics we show that deep neural networks learn best when they are at quasi-criticality, just like brains. Take a look at our paper!
(Some) neural embeddings, like node2vec, are surprisingly good at learning community structure! Check our paper in Nature Communications.
Over 92% of authors of papers on Covid-19 knew nothing about infectious diseases! Problem or opportunity? Both! Check out our comment in Nature Human Behavior.
Last modified on January the 24th, 2026