Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Proceedings of the 15th international conference on World Wide Web
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Proceedings of the forty-first annual ACM symposium on Theory of computing
Scalable graph clustering using stochastic flows: applications to community discovery
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-evolution of social and affiliation networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
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
Community detection in Social Media
Data Mining and Knowledge Discovery
DEMON: a local-first discovery method for overlapping communities
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
RolX: structural role extraction & mining in large graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Overlapping community detection at scale: a nonnegative matrix factorization approach
Proceedings of the sixth ACM international conference on Web search 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
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
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Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks. In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.