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Creators/Authors contains: "Sariyuce, Ahmet Erdem"

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  1. ; ; (, Springer Nature Switzerland)
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  2. ; ; ; (, Lecture notes in computer science)
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  3. ; (, 2020 IEEE International Conference on Big Data (Big Data))
    null (Ed.)
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  4. ; (, Lecture notes in computer science)
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  5. ; ; ; ; (, Proceedings of the SIAM International Conference on Data Mining)
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  6. ; ; (, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)
    We consider the problem of counting motifs in bipartite affiliation networks, such as author-paper, user-product, and actor-movie relations. We focus on counting the number of occurrences of a "butterfly", a complete 2x2 biclique, the simplest cohesive higher-order structure in a bipartite graph. Our main contribution is a suite of randomized algorithms that can quickly approximate the number of butterflies in a graph with a provable guarantee on accuracy. An experimental evaluation on large real-world networks shows that our algorithms return accurate estimates within a few seconds, even for networks with trillions of butterflies and hundreds of millions of edges. 
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