Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
Convex Optimization
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the forty-first annual ACM symposium on Theory of computing
Co-evolution of social and affiliation networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast coordinate descent methods with variable selection for non-negative matrix factorization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Computer Science Review
Low rank modeling of signed networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Community-Affiliation Graph Model for Overlapping Network Community Detection
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Defining and Evaluating Network Communities Based on Ground-Truth
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Information cartography: creating zoomable, large-scale maps of information
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Computer science fields as ground-truth communities: their impact, rise and fall
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and 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
High quality, scalable and parallel community detection for large real graphs
Proceedings of the 23rd international conference on World wide web
Hi-index | 0.00 |
Network communities represent basic structures for understanding the organization of real-world networks. A community (also referred to as a module or a cluster) is typically thought of as a group of nodes with more connections amongst its members than between its members and the remainder of the network. Communities in networks also overlap as nodes belong to multiple clusters at once. Due to the difficulties in evaluating the detected communities and the lack of scalable algorithms, the task of overlapping community detection in large networks largely remains an open problem. In this paper we present BIGCLAM (Cluster Affiliation Model for Big Networks), an overlapping community detection method that scales to large networks of millions of nodes and edges. We build on a novel observation that overlaps between communities are densely connected. This is in sharp contrast with present community detection methods which implicitly assume that overlaps between communities are sparsely connected and thus cannot properly extract overlapping communities in networks. In this paper, we develop a model-based community detection algorithm that can detect densely overlapping, hierarchically nested as well as non-overlapping communities in massive networks. We evaluate our algorithm on 6 large social, collaboration and information networks with ground-truth community information. Experiments show state of the art performance both in terms of the quality of detected communities as well as in speed and scalability of our algorithm.