Flows in Undirected Unit Capacity Networks
SIAM Journal on Discrete Mathematics
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Strategic network formation with structural holes
Proceedings of the 9th ACM conference on Electronic commerce
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting high log-densities: an O(n¼) approximation for densest k-subgraph
Proceedings of the forty-second ACM symposium on Theory of computing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Detecting the structure of social networks using (α, β)-communities
WAW'11 Proceedings of the 8th international conference on Algorithms and models for the web graph
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Polynomial integrality gaps for strong SDP relaxations of Densest k-subgraph
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Detecting Community Kernels in Large Social Networks
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Inferring social ties across heterogenous networks
Proceedings of the fifth ACM international conference on Web search and data mining
Dinitz' algorithm: the original version and even's version
Theoretical Computer Science
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PatentMiner: topic-driven patent analysis and mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Confluence: conformity influence in large social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
SAE: social analytic engine for large networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting stochastic temporal network motifs for human communication patterns analysis
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Socially-based brokerage and composition in virtual communities
International Journal of Networking and Virtual Organisations
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
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The theory of structural holes suggests that individuals would benefit from filling the "holes" (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information diffusion. However, there is still lack of a principled methodology to detect structural hole spanners from a given social network. In this work, we precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models have been developed to implement the objective function. For the first model, we present an exact algorithm to solve it and prove its convergence. As for the second model, the optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees. We test the proposed models on three different networks: Coauthor, Twitter, and Inventor. Our study provides evidence for the theory of structural holes, e.g., 1% of Twitter users who span structural holes control 25% of the information diffusion on Twitter. We compare the proposed models with several alternative methods and the results show that our models clearly outperform the comparison methods. Our experiments also demonstrate that the detected structural hole spanners can help other social network applications, such as community kernel detection and link prediction. To the best of our knowledge, this is the first attempt to address the problem of mining structural hole spanners in large social networks.