{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:30:48Z","timestamp":1770273048853,"version":"3.49.0"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:p>\n            For a temporal graph like transaction network, finding a densely connected subgraph that contains a vertex like a suspicious account during a period is valuable. Thus, we study the Temporal\n            <jats:italic>k<\/jats:italic>\n            -Core Component Search (TCCS) problem, which aims to find a connected component of temporal\n            <jats:italic>k<\/jats:italic>\n            -core for any given vertex and time interval. Towards this goal, we propose a novel Evolution Forest Index (EF-Index) that can address TCCS in optimal time. Essentially, EF-Index leverages the evolutionary order on temporal\n            <jats:italic>k<\/jats:italic>\n            -cores to both compress the connectivity between vertices in temporal\n            <jats:italic>k<\/jats:italic>\n            -cores of all time intervals into a minimum set of compactest Minimum Temporal Spanning Forests (MTSFs) and retrieve MTSF for a given time interval rapidly. Here, a crucial innovation is that, we extend the temporal\n            <jats:italic>k<\/jats:italic>\n            -core evolution theory by introducing a pair of time-topology isomorphic relations, on top of which the evolutionary order in topology domain can be simply computed by a \"kernel function\" in time domain. Moreover, we design an efficient mechanism to update EF-Index incrementally for dynamic edge streams. The experimental results on a variety of real-world temporal graphs demonstrate that, EF-Index outperforms the state-of-the-art approach by 1--3 orders of magnitude on processing TCCS, and its space overhead is reduced by 4--5 orders of magnitude compared with preserving connectivity uncompressedly.\n          <\/jats:p>","DOI":"10.14778\/3681954.3681967","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T16:23:36Z","timestamp":1725035016000},"page":"2840-2853","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Evolution Forest Index: Towards Optimal Temporal\n            <i>k<\/i>\n            -Core Component Search via Time-Topology Isomorphic Computation"],"prefix":"10.14778","volume":"17","author":[{"given":"Junyong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"given":"Ming","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"given":"Yuanyuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"given":"Tieyun","family":"Qian","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}]},{"given":"Mengchi","family":"Liu","sequence":"additional","affiliation":[{"name":"South China Normal University, Guangzhou, China"}]},{"given":"Jeffrey Xu","family":"Yu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.11.003"},{"key":"e_1_2_1_2_1","unstructured":"Xinwei Cai Xiangyu Ke Kai Wang Lu Chen Tianming Zhang Qing Liu and Yunjun Gao. 2023. 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SNAP Datasets: Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00077"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3168343"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-021-00917-z"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.14778\/3446095.3446099"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389697"},{"key":"e_1_2_1_26_1","volume-title":"A Guide to Temporal Networks","author":"Masuda Naoki","unstructured":"Naoki Masuda and Renaud Lambiotte. 2020. A Guide to Temporal Networks. World Scientific Publishing Europe Ltd."},{"key":"e_1_2_1_27_1","volume-title":"Italiano","author":"Oettershagen Lutz","year":"2023","unstructured":"Lutz Oettershagen, Athanasios L. Konstantinidis, and Giuseppe F. Italiano. 2023. 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A Unified and Scalable Algorithm Framework of User-Defined Temporal (k X)-Core Query. 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