Tag Archives: community detection

New paper in Nature Physics

July 29, 2022News, Santocommunity detection, complex networkssanto

In 2002 the paper Community structure in social and biological networks, by Michelle Girvan and Mark E. J. Newman, marked the beginning of network community detection, possibly the most popular topic in network science, which tackles the problem of automatically discovering communities — groups of nodes of the network that are strongly connected or that share similar features or roles.

Twenty years later, it’s time to see how the field is doing. In the Comment 20 years of network community detection, just published in Nature Physics, Santo Fortunato and Mark Newman present a brief overview of this fascinating topic and highlight future directions.

DREAM Challenge paper published in Nature Methods

August 31, 2019News, Santocommunity detection, genetic networkssanto

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.

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CNetS team studies generalized modularity in complex networks & Systems

December 23, 2015NewsCASCI, community detection, complex networks, complex systems, dynamics, modularity

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.