Clique partitions, graph compression and speeding-up algorithms
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Towards Compressing Web Graphs
DCC '01 Proceedings of the Data Compression Conference
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
ACM SIGKDD Explorations Newsletter
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Metropolis Algorithms for Representative Subgraph Sampling
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Expanding network communities from representative examples
ACM Transactions on Knowledge Discovery from Data (TKDD)
Expansion and search in networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Correcting for missing data in information cascades
Proceedings of the fourth ACM international conference on Web search and data mining
Local graph sparsification for scalable clustering
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Expansion properties of large social graphs
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Benefits of bias: towards better characterization of network sampling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Diversified ranking on large graphs: an optimization viewpoint
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Rumor spreading and vertex expansion
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Leveraging trust and distrust for sybil-tolerant voting in online social media
Proceedings of the 1st Workshop on Privacy and Security in Online Social Media
Community detection in Social Media
Data Mining and Knowledge Discovery
Coarse-grained topology estimation via graph sampling
Proceedings of the 2012 ACM workshop on Workshop on online social networks
Space-efficient sampling from social activity streams
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Sampling connected induced subgraphs uniformly at random
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Sampling bias in user attribute estimation of OSNs
Proceedings of the 22nd international conference on World Wide Web companion
Efficient community detection in large networks using content and links
Proceedings of the 22nd international conference on World Wide Web
Discovering implicit communities in Web forums through ontologies
Web Intelligence and Agent Systems
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We propose a novel method, based on concepts from expander graphs, to sample communities in networks. We show that our sampling method, unlike previous techniques, produces subgraphs representative of community structure in the original network. These generated subgraphs may be viewed as stratified samples in that they consist of members from most or all communities in the network. Using samples produced by our method, we show that the problem of community detection may be recast into a case of statistical relational learning. We empirically evaluate our approach against several real-world datasets and demonstrate that our sampling method can effectively be used to infer and approximate community affiliation in the larger network.