Mining frequent patterns in evolving graphs
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A4 Artikkeli konferenssijulkaisussa
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en
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10
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CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 923-932
Abstract
Given a labeled graph, the frequent-subgraph mining (FSM) problem asks to find all the k-vertex subgraphs that appear with frequency greater than a given threshold. FSM has numerous applications ranging from biology to network science, as it provides a compact summary of the characteristics of the graph. However, the task is challenging, even more so for evolving graphs due to the streaming nature of the input and the exponential time complexity of the problem. In this paper, we initiate the study of the approximate FSM problem in both incremental and fully-dynamic streaming settings, where arbitrary edges can be added or removed from the graph. For each streaming setting, we propose algorithms that can extract a high-quality approximation of the frequent k-vertex subgraphs for a given threshold, at any given time instance, with high probability. In contrast to the existing state-of-the-art solutions that require iterating over the entire set of subgraphs for any update, our algorithms operate by maintaining a uniform sample of k-vertex subgraphs with optimized neighborhood-exploration procedures local to the updates. We provide theoretical analysis of the proposed algorithms and empirically demonstrate that the proposed algorithms generate high-quality results compared to baselines.Description
| openaire: EC/H2020/654024/EU//SoBigData QC 20180312
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Aslay, C, Nasir, M A U, De Francisci Morales, G & Gionis, A 2018, Mining frequent patterns in evolving graphs. in N Paton, S Candan, H Wang, J Allan, R Agrawal, A Labrinidis, A Cuzzocrea, M Zaki, D Srivastava, A Broder & A Schuster (eds), CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, pp. 923-932, ACM International Conference on Information and Knowledge Management, Torino, Italy, 22/10/2018. https://doi.org/10.1145/3269206.3271772