DEMON: a local-first discovery method for overlapping communities

By Michele Coscia, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi. 2012

266 citations recorded by the source corpus.

Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Debiasing Community Detection: The Importance of Lowly Connected Nodes (2019, methodology): “Other methods of such type that do not utilize node attributes exist, such as BigCLAM and DEMON [6, 20].”
Detecting cohesive and 2-mode communities indirected and undirected networks (2014, methodology): “For comparison we consider the following baseline methods: MMSB (Mixed Membership Stochastic Blockmodels) [3], which can detect both cohesive and 2-mode communities in undirected networks and is extremely slow; Clique Percolation, [34] Link Clustering [2], BigCLAM [43, 45] are state of the art overl”