Matrix multiplication via arithmetic progressions
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
IEEE Transactions on Mobile Computing
Counting triangles in data streams
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Fast computation of low-rank matrix approximations
Journal of the ACM (JACM)
Efficient semi-streaming algorithms for local triangle counting in massive graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Counting of Triangles in Large Real Networks without Counting: Algorithms and Laws
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
DOULION: counting triangles in massive graphs with a coin
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral Counting of Triangles in Power-Law Networks via Element-Wise Sparsification
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
SpamWatcher: a streaming social network analytic on the IBM wire-speed processor
Proceedings of the 5th ACM international conference on Distributed event-based system
MODM: multi-objective diffusion model for dynamic social networks using evolutionary algorithm
The Journal of Supercomputing
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The Clustering Coefficient (CC) is a fundamental measure in social network analysis assessing the degree to which nodes tend to cluster together. While CC computation on static graphs is well studied, emerging applications have new requirements for online query of the "global" CC of a given subset of a graph. As social networks are widely stored in databases for easy updating and accessing, computing CC of their subset becomes a time-consuming task, especially when the network grows large and cannot fit in memory. This paper presents a novel method called "Approximate Neighborhood Index (ANI)" to significantly reduce the query latency for CC computation compared to traditional SQL based database queries. A Bloom-filter-like data structure is leveraged to construct ANI in front of a relational database. Experimental results show that the proposed approach can guarantee the correctness of a CC query while significantly reducing the query latency at a reasonable memory cost.