Multi-relation graph summarization
Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization su...
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sg-ntu-dr.10356-1629842022-11-14T07:23:34Z Multi-relation graph summarization Ke, Xiangyu Khan, Arijit Bonchi, Francesco School of Computer Science and Engineering Engineering::Computer science and engineering Graph Summarization Multi-Relation Graph Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this paper, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive model of real-world activities, in which a relation can be a topic in social networks, an interaction type in genetic networks, or a snapshot in temporal graphs. The first approach that we consider for multi-relation graph summarization is a two-step method based on summarizing each relation in isolation, and then aggregating the resulting summaries in some clever way to produce a final unique summary. In doing this, as a side contribution, we provide the first polynomial-time approximation algorithm based on the k-Median clustering for the classic problem of lossless single-relation graph summarization. Then, we demonstrate the shortcomings of these two-step methods, and propose holistic approaches, both approximate and heuristic algorithms, to compute a summary directly for multi-relation graphs. In particular, we prove that the approximation bound of k-Median clustering for the single relation solution can be maintained in a multi-relation graph with proper aggregation operation over adjacency matrices corresponding to its multiple relations. Experimental results and case studies (on co-authorship networks and brain networks) validate the effectiveness and efficiency of the proposed algorithms. Ministry of Education (MOE) Arijit Khan is supported by MOE Tier 1 and Tier 2 grants RG117/19 and MOE2019T2-2-042. 2022-11-14T07:23:34Z 2022-11-14T07:23:34Z 2022 Journal Article Ke, X., Khan, A. & Bonchi, F. (2022). Multi-relation graph summarization. ACM Transactions On Knowledge Discovery From Data, 16(5), 82.1-82.30. https://dx.doi.org/10.1145/3494561 1556-4681 https://hdl.handle.net/10356/162984 10.1145/3494561 2-s2.0-85131167028 5 16 82.1 82.30 en RG117/19 MOE2019T2-2-042 ACM Transactions on Knowledge Discovery from Data © 2022 held by the owner/author(s). Publication rights licensed to ACM. All rights reserved. |
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Engineering::Computer science and engineering Graph Summarization Multi-Relation Graph Ke, Xiangyu Khan, Arijit Bonchi, Francesco Multi-relation graph summarization |
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Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In
this paper, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive model of real-world activities, in which a relation can be a topic in social networks, an interaction type in genetic networks, or a snapshot in temporal graphs. The first approach that we consider for multi-relation graph summarization is a two-step method based on summarizing each relation in isolation, and then aggregating the resulting summaries in some clever way to produce a final unique summary. In doing this, as a side contribution, we provide the first polynomial-time approximation algorithm based on the k-Median clustering for
the classic problem of lossless single-relation graph summarization. Then, we demonstrate the shortcomings of these two-step methods, and propose holistic approaches, both approximate and heuristic algorithms, to compute a summary directly for multi-relation graphs. In particular, we prove that the approximation bound of k-Median clustering for the single relation solution can
be maintained in a multi-relation graph with proper aggregation operation over adjacency matrices corresponding to its multiple relations. Experimental results and case studies (on co-authorship networks and brain networks) validate the effectiveness and efficiency of the proposed algorithms. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Ke, Xiangyu Khan, Arijit Bonchi, Francesco |
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Ke, Xiangyu Khan, Arijit Bonchi, Francesco |
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Ke, Xiangyu |
title |
Multi-relation graph summarization |
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Multi-relation graph summarization |
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Multi-relation graph summarization |
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Multi-relation graph summarization |
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Multi-relation graph summarization |
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multi-relation graph summarization |
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2022 |
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https://hdl.handle.net/10356/162984 |
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