Workshop: Community Detection and Networks Reconstruction
Third workshop in the Stochastic Activity Month on NETWORKS.
Recent advances in the study of networks in the physics, mathematics and computer science communities have provided novel probabilistic tools to address various network-related problems of practical relevance. Two seemingly different, but actually intimately related, problems are community detection and network reconstruction. Community detection is the identification of groups of nodes that share some property (e.g., are densely connected with each other in a given real-world network). This seemingly simple task presents us with several computational and analytical challenges, even in very simple and idealized scenarios. Network reconstruction problems, on the other hand, attempt to infer features of a real-world network, of which only partial and/or aggregate information is available (e.g., inference from a sample of a subset of the network).
These two broad classes of problems are conveniently tackled using random graph models that preserve some observed features of the real network, while providing a probabilistic estimate for the unobserved features. This workshops focuses on various aspects of community detection and network reconstruction. Emphasis will be put on the interdisciplinary nature of both problems, and contributions will highlight both the applied and the theoretical side.