The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Hierarchical, Parameter-Free Community Discovery
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
GuruMine: A Pattern Mining System for Discovering Leaders and Tribes
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Proceedings of the 20th international conference on World wide web
Estimating sizes of social networks via biased sampling
Proceedings of the 20th international conference on World wide web
Information spreading in context
Proceedings of the 20th international conference on World wide web
A classification for community discovery methods in complex networks
Statistical Analysis and Data Mining
Overlapping community detection using seed set expansion
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Detecting cohesive and 2-mode communities indirected and undirected networks
Proceedings of the 7th ACM international conference on Web search and data mining
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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.