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Efficient analytical computation of expected frequency of motifs of small size by marginalization in uncertain network

Published: 19 January 2022 Publication History

Abstract

Counting motifs in an uncertain graph for which each link is associated with a connection probability is computationally expensive when the graph is huge due to the extremely large number of possible worlds. Natural approach is to rely on sampling-based approximation methods, but this still needs many sample graphs for obtaining accurate results. We propose a novel method that analytically computes the expected frequency of motif without relying on expensive sampling. Marginalizing the probability of each possible world on a candidate motif can drastically reduce the number of possible worlds that need be considered when the size of motif is small. Experiments using real-world data confirm that the proposed method is effective and efficient. It is far better than the state-of-the-art sampling-based method. The accuracy is guaranteed and the running time is about 4 order of magnitude faster. It runs at a speed that does not depend on the connection probability.

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  • (2022)Efficient computation of expected motif frequency in uncertain graphs by exploiting possible world marginalization and motif transitionSocial Network Analysis and Mining10.1007/s13278-022-00956-y12:1Online publication date: 3-Sep-2022

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            cover image ACM Conferences
            ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
            November 2021
            693 pages
            ISBN:9781450391283
            DOI:10.1145/3487351
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            Published: 19 January 2022

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            • (2022)Efficient computation of expected motif frequency in uncertain graphs by exploiting possible world marginalization and motif transitionSocial Network Analysis and Mining10.1007/s13278-022-00956-y12:1Online publication date: 3-Sep-2022

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