USIWO: A Local Community Search Algorithm for Uncertain Graphs
Authors: 
Yashar Talebirad, Mohammadmahdi Zafarmand, 
Osmar R. Zaiane, Christine LargeronAuthors Info & Claims
Yashar Talebirad, Mohammadmahdi Zafarmand, Osmar R. Zaiane, Christine LargeronAuthors Info & ClaimsPages 187 - 194
Abstract
Community detection and community search are both critical tasks in graph mining, each serving unique purposes and presenting distinct challenges. The former aims to partition the graph vertices into densely connected subsets, while the latter adopts a more ego-centric approach, focusing on a specific node or group of nodes to identify a densely-connected subgraph that contains these query nodes. However, many real-world networks are characterized by uncertainty, leading to the notion of uncertain or probabilistic graphs. The transition from deterministic graphs to uncertain graphs introduces new challenges. We present USIWO, an efficient and practical solution for community search in unweighted uncertain graphs with edge uncertainty. In addition to being accurate, the approach utilizes an efficient data structure for storing only the relevant parts of the network in main memory, eliminating the need to store the entire graph, making it a valuable tool in finding the core of a community on very large uncertain graphs, when there is limited time and memory available. The algorithm operates through a one-node-expansion approach, based on the concepts of strong and weak links within a graph. Experimental results on several datasets demonstrate the algorithm's efficiency and performance.
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Publication History
Published: 15 March 2024
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- Research-article
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- NSERC RGPIN-2020-04440 Zaiane
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ASONAM '23
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ASONAM '23: International Conference on Advances in Social Networks Analysis and Mining
November 6 - 9, 2023
Kusadasi, Turkiye
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ASONAM '23 Paper Acceptance Rate 53 of 145 submissions, 37%;
Overall Acceptance Rate 116 of 549 submissions, 21%
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