DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Shin, Kijung | - |
dc.contributor.advisor | 신기정 | - |
dc.contributor.author | Shin, Hyeonjeong | - |
dc.date.accessioned | 2023-06-22T19:31:13Z | - |
dc.date.available | 2023-06-22T19:31:13Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032323&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308183 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2023.2,[iv, 27 p. :] | - |
dc.description.abstract | A variety of tasks on dynamic graphs, including anomaly detection, community detection, compression, and graph understanding, have been formulated as problems of identifying constituent (near) bi-cliques (i.e., complete bipartite graphs). Even when we restrict our attention to maximal ones, there can be exponentially many near bi-cliques, and thus finding all of them is practically impossible for large graphs. Then, two questions naturally arise: (Q1) What is a “good” set of near bi-cliques? That is, given a set of near bi-cliques in the input dynamic graph, how should we evaluate its quality? (Q2) Given a large dynamic graph, how can we rapidly identify a high-quality set of near bi-cliques in it? Regarding Q1, we measure how concisely, precisely, and exhaustively a given set of near bi-cliques describes the input dynamic graph. We combine these three perspectives systematically on the Minimum Description Length principle. Regarding Q2, we propose CutNPeel, a fast search algorithm for a high-quality set of near bi-cliques. By adaptively re-partitioning the input graph, CutNPeel reduces the search space and at the same time improves the search quality. Our experiments using six real-world dynamic graphs demonstrate that CutNPeel is (a) High-quality: providing near bi-cliques of up to 51.2% better quality than its state-of-the-art competitors, (b) Fast: up to 68.8× faster than the next-best competitor, and (c) Scalable: scaling to graphs with 134 million edges. We also show successful applications of CutNPeel to graph compression and pattern discovery. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Bi-clique▼aDynamic graph▼aGraph compression▼aPattern discovery | - |
dc.subject | 이분 그래프▼a동적 그래프▼a그래프 압축▼a패턴 마이닝 | - |
dc.title | Finding a concise, precise, and exhaustive set of near bi-cliques in dynamic graphs | - |
dc.title.alternative | 동적 그래프에서 정확한, 완전한, 그리고 간결한 근사 완전 이분 부분 그래프 집합의 탐색 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :김재철AI대학원, | - |
dc.contributor.alternativeauthor | 신현정 | - |
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