DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choe, Min Young | ko |
dc.contributor.author | Yoo, Jaemin | ko |
dc.contributor.author | Lee, Geon | ko |
dc.contributor.author | Baek, Woonsung | ko |
dc.contributor.author | Kang, U | ko |
dc.contributor.author | Shin, Kijung | ko |
dc.date.accessioned | 2022-10-06T07:00:20Z | - |
dc.date.available | 2022-10-06T07:00:20Z | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.issued | 2022-04-25 | - |
dc.identifier.citation | 31st ACM World Wide Web Conference, WWW 2022, pp.1080 - 1092 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298888 | - |
dc.description.abstract | Graphs are widely used for representing pairwise interactions in complex systems. Since such real-world graphs are large and often evergrowing, sampling a small representative subgraph is indispensable for various purposes: simulation, visualization, stream processing, representation learning, crawling, to name a few. However, many complex systems consist of group interactions (e.g., collaborations of researchers and discussions on online Q&A platforms), and thus they can be represented more naturally and accurately by hypergraphs (i.e., sets of sets) than by ordinary graphs. Motivated by the prevalence of large-scale hypergraphs, we study the problem of representative sampling from real-world hypergraphs, aiming to answer (Q1) what a representative sub-hypergraph is and (Q2) how we can find a representative one rapidly without an extensive search. Regarding Q1, we propose to measure the goodness of a sub-hypergraph by comparing it with the entire hypergraph in terms of ten graph-level, hyperedge-level, and node-level statistics. Regarding Q2, we first analyze the characteristics of six intuitive approaches in 11 real-world hypergraphs. Then, based on the analysis, we propose MiDaS, which draws hyperedges with a bias towards those with high-degree nodes. Through extensive experiments, we demonstrate that MiDaS is (a) Representative: finding overall the most representative samples among 13 considered approaches, (b) Fast: several orders of magnitude faster than the strongest competitors, which performs an extensive search, and (c) Automatic: rapidly searching a proper degree of bias. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | MiDaS: Representative Sampling from Real-world Hypergraphs | - |
dc.type | Conference | - |
dc.identifier.wosid | 000852713001015 | - |
dc.identifier.scopusid | 2-s2.0-85129791363 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1080 | - |
dc.citation.endingpage | 1092 | - |
dc.citation.publicationname | 31st ACM World Wide Web Conference, WWW 2022 | - |
dc.identifier.conferencecountry | FR | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1145/3485447.3512157 | - |
dc.contributor.localauthor | Yoo, Jaemin | - |
dc.contributor.localauthor | Shin, Kijung | - |
dc.contributor.nonIdAuthor | Baek, Woonsung | - |
dc.contributor.nonIdAuthor | Kang, U | - |
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