{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T05:42:12Z","timestamp":1761716532130,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":75,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"funder":[{"name":"Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation"},{"name":"MUR (Ministry of University and Research) of Italy","award":["PRIN n. 2022TS4Y3N, CN00000013"],"award-info":[{"award-number":["PRIN n. 2022TS4Y3N, CN00000013"]}]},{"name":"EC H2020 RIA project SoBigData++","award":["871042"],"award-info":[{"award-number":["871042"]}]},{"name":"ERC Advanced Grant REBOUND","award":["834862"],"award-info":[{"award-number":["834862"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671889","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"2536-2547","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Scalable Temporal Motif Densest Subnetwork Discovery"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5894-0774","authenticated-orcid":false,"given":"Ilie","family":"Sarpe","sequence":"first","affiliation":[{"name":"KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2244-2320","authenticated-orcid":false,"given":"Fabio","family":"Vandin","sequence":"additional","affiliation":[{"name":"University of Padova, Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5211-112X","authenticated-orcid":false,"given":"Aristides","family":"Gionis","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology, Stockholm, Sweden"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Walid Ahmad Mason A. Porter and Mariano Beguerisse-Diaz. 2021. Tie-Decay Networks in Continuous Time and Eigenvector-Based Centralities. TNSE.","DOI":"10.1109\/TNSE.2021.3071429"},{"key":"e_1_3_2_2_2_1","volume-title":"Rossi","author":"Ahmed Nesreen K.","year":"2021","unstructured":"Nesreen K. Ahmed, Nick Duffield, and Ryan A. Rossi. 2021. Online Sampling of Temporal Networks. TKDD."},{"key":"e_1_3_2_2_3_1","volume-title":"Ben Steer, Raul Mondragon, Felix Cuadrado, Renaud Lambiotte, and Richard G. Clegg.","author":"Arnold Naomi A.","year":"2024","unstructured":"Naomi A. Arnold, Peijie Zhong, Cheick Tidiane Ba, Ben Steer, Raul Mondragon, Felix Cuadrado, Renaud Lambiotte, and Richard G. Clegg. 2024. Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks. arXiv."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Bahman Bahmani Ravi Kumar and Sergei Vassilvitskii. 2012. Densest Subgraph in Streaming and MapReduce. PVLDB.","DOI":"10.14778\/2140436.2140442"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Caleb Belth Xinyi Zheng and Danai Koutra. 2020. Mining Persistent Activity in Continually Evolving Networks. KDD.","DOI":"10.1145\/3394486.3403136"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Sayan Bhattacharya Monika Henzinger Danupon Nanongkai and Charalampos Tsourakakis. 2015. Space- and Time-Efficient Algorithm for Maintaining Dense Subgraphs on One-Pass Dynamic Streams. STOC.","DOI":"10.1145\/2746539.2746592"},{"key":"e_1_3_2_2_7_1","volume-title":"Pardalos","author":"Boginski Vladimir","year":"2003","unstructured":"Vladimir Boginski, Sergiy Butenko, and Panos M. Pardalos. 2003. On Structural Properties of the Market Graph. Innovations in Financial and Economic Networks."},{"key":"e_1_3_2_2_8_1","volume-title":"Flowless: Extracting Densest Subgraphs Without Flow Computations. WWW.","author":"Boob Digvijay","year":"2020","unstructured":"Digvijay Boob, Yu Gao, Richard Peng, Saurabh Sawlani, Charalampos Tsourakakis, Di Wang, and Junxing Wang. 2020. Flowless: Extracting Densest Subgraphs Without Flow Computations. WWW."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Y. Boykov and V. Kolmogorov. 2004. An experimental comparison of min-cut\/max-flow algorithms for energy minimization in vision. TPAMI.","DOI":"10.1109\/TPAMI.2004.60"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Marco Bressan Stefano Leucci and Alessandro Panconesi. 2021. Faster Motif Counting via Succinct Color Coding and Adaptive Sampling. TKDD.","DOI":"10.1145\/3447397"},{"key":"e_1_3_2_2_11_1","unstructured":"Xinwei Cai Xiangyu Ke Kai Wang Lu Chen Tianming Zhang Qing Liu and Yunjun Gao. 2023. Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs. PVLDB."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Moses Charikar. 2000. Greedy Approximation Algorithms for Finding Dense Components in a Graph. Approximation Algorithms for Combinatorial Optimization.","DOI":"10.1007\/3-540-44436-X_10"},{"key":"e_1_3_2_2_13_1","volume-title":"Torres","author":"Chekuri Chandra","year":"2022","unstructured":"Chandra Chekuri, Kent Quanrud, and Manuel R. Torres. 2022. Densest Subgraph: Supermodularity, Iterative Peeling, and Flow. SODA."},{"key":"e_1_3_2_2_14_1","volume-title":"Tsourakakis","author":"Chen Tianyi","year":"2023","unstructured":"Tianyi Chen, Brian Matejek, Michael Mitzenmacher, and Charalampos E. Tsourakakis. 2023. Algorithmic Tools for Understanding the Motif Structure of Networks. ECML PKDD."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Tianyi Chen and Charalampos Tsourakakis. 2022. AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks. KDD.","DOI":"10.1145\/3534678.3539100"},{"volume-title":"Evolution of Networks","author":"Dorogovtsev S.N.","key":"e_1_3_2_2_16_1","unstructured":"S.N. Dorogovtsev and J.F.F. Mendes. 2003. Evolution of Networks. Oxford University Press."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Alessandro Epasto Silvio Lattanzi and Mauro Sozio. 2015. Efficient Densest Subgraph Computation in Evolving Graphs. WWW.","DOI":"10.1145\/2736277.2741638"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Yixiang Fang Wensheng Luo and Chenhao Ma. 2022. Densest subgraph discovery on large graphs. PVLDB.","DOI":"10.14778\/3554821.3554895"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Yixiang Fang Kaiqiang Yu Reynold Cheng Laks V. S. Lakshmanan and Xuemin Lin. 2019. Efficient algorithms for densest subgraph discovery. PVLDB.","DOI":"10.14778\/3342263.3342645"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Adriano Fazzone Tommaso Lanciano Riccardo Denni Charalampos E. Tsourakakis and Francesco Bonchi. 2022. Discovering Polarization Niches via Dense Subgraphs with Attractors and Repulsers. PVLDB.","DOI":"10.14778\/3565838.3565843"},{"volume-title":"The development of social network analysis","author":"Freeman Linton C.","key":"e_1_3_2_2_21_1","unstructured":"Linton C. Freeman. 2004. The development of social network analysis. Empirical Press."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Edoardo Galimberti Martino Ciaperoni Alain Barrat Francesco Bonchi Ciro Cattuto and Francesco Gullo. 2020. Span-core Decomposition for Temporal Networks. TKDD.","DOI":"10.1145\/3418226"},{"key":"e_1_3_2_2_23_1","unstructured":"Zhongqiang Gao Chuanqi Cheng Yanwei Yu Lei Cao Chao Huang and Junyu Dong. 2022. Scalable Motif Counting for Large-scale Temporal Graphs. ICDE."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Aristides Gionis Lutz Oettershagen and Ilie Sarpe. 2024. Mining Temporal Networks. WWW.","DOI":"10.1145\/3589335.3641245"},{"key":"e_1_3_2_2_25_1","volume-title":"Tsourakakis","author":"Gionis Aristides","year":"2015","unstructured":"Aristides Gionis and Charalampos E. Tsourakakis. 2015. Dense Subgraph Discovery. KDD."},{"key":"e_1_3_2_2_27_1","article-title":"Recent Advances in Fully Dynamic Graph Algorithms","author":"Hanauer Kathrin","year":"2021","unstructured":"Kathrin Hanauer, Monika Henzinger, and Christian Schulz. 2021. Recent Advances in Fully Dynamic Graph Algorithms. Journal of Experimental Algorithmics.","journal-title":"Journal of Experimental Algorithmics."},{"key":"e_1_3_2_2_28_1","unstructured":"Yizhang He Kai Wang Wenjie Zhang Xuemin Lin and Ying Zhang. 2023. Scaling Up k-Clique Densest Subgraph Detection. PACMMOD."},{"key":"e_1_3_2_2_29_1","volume-title":"McGuffin","author":"Henry Nathalie","year":"2007","unstructured":"Nathalie Henry, Jean-Daniel Fekete, and Michael J. McGuffin. 2007. NodeTrix: a Hybrid Visualization of Social Networks. TVCG."},{"volume-title":"Temporal Network Theory","author":"Holme Petter","key":"e_1_3_2_2_30_1","unstructured":"Petter Holme and Jari Saram\u00e4ki. 2019. Temporal Network Theory. Springer International Publishing."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Petter Holme and Jari Saram\u00e4ki. 2012. Temporal networks. Physics Reports.","DOI":"10.1007\/978-3-642-36461-7"},{"key":"e_1_3_2_2_32_1","volume-title":"Scott Shenker, and Ion Stoica.","author":"Huebsch Ryan","year":"2003","unstructured":"Ryan Huebsch, Joseph M. Hellerstein, Nick Lanham, Boon Thau Loo, Scott Shenker, and Ion Stoica. 2003. Querying the Internet with PIER. PVLDB."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Shweta Jain and C. Seshadhri. 2017. A Fast and Provable Method for Estimating Clique Counts Using Tur\u00e1n's Theorem. WWW.","DOI":"10.1145\/3038912.3052636"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Lauri Kovanen M\u00e1rton Karsai Kimmo Kaski J\u00e1nos Kert\u00e9sz and Jari Saram\u00e4ki. 2011. Temporal motifs in time-dependent networks. JSTAT.","DOI":"10.1088\/1742-5468\/2011\/11\/P11005"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Renaud Lambiotte Lionel Tabourier and Jean-Charles Delvenne. 2013. Burstiness and spreading on temporal networks. The European Physical Journal B.","DOI":"10.1140\/epjb\/e2013-40456-9"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Tommaso Lanciano Atsushi Miyauchi Adriano Fazzone and Francesco Bonchi. 2023. A Survey on the Densest Subgraph Problem and its Variants. Computing Surveys.","DOI":"10.1145\/3653298"},{"key":"e_1_3_2_2_37_1","unstructured":"Jinsoo Lee Wook-Shin Han Romans Kasperovics and Jeong-Hoon Lee. 2012. An in-depth Comparison of Subgraph Isomorphism Algorithms in Graph Databases. PVLDB."},{"key":"e_1_3_2_2_38_1","unstructured":"Victor E. Lee Ning Ruan Ruoming Jin and Charu Aggarwal. 2010. A Survey of Algorithms for Dense Subgraph Discovery. Managing and Mining Graph Data."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Da Lei Xuewu Chen Long Cheng Lin Zhang Satish V. Ukkusuri and Frank Witlox. 2020. Inferring Temporal Motifs for Travel Pattern Analysis using Large Scale Smart Card Data. Transportation Research Part C: Emerging Technologies.","DOI":"10.1016\/j.trc.2020.102810"},{"key":"e_1_3_2_2_40_1","volume-title":"Jeffrey Xu Yu, and Qiangqiang Dai","author":"Li Rong-Hua","year":"2018","unstructured":"Rong-Hua Li, Jiao Su, Lu Qin, Jeffrey Xu Yu, and Qiangqiang Dai. 2018. Persistent Community Search in Temporal Networks. ICDE."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3071721"},{"key":"e_1_3_2_2_42_1","article-title":"Fishing for Fraudsters: Uncovering Ethereum Phishing Gangs With Blockchain Data","author":"Liu Jieli","year":"2024","unstructured":"Jieli Liu, Jinze Chen, Jiajing Wu, Zhiying Wu, Junyuan Fang, and Zibin Zheng. 2024. Fishing for Fraudsters: Uncovering Ethereum Phishing Gangs With Blockchain Data. Transactions on Information Forensics and Security.","journal-title":"Transactions on Information Forensics and Security."},{"key":"e_1_3_2_2_43_1","unstructured":"Penghang Liu Rupam Acharyya Robert E. Tillman Shunya Kimura Naoki Masuda and Ahmet Erdem Sariy\u00fcce. 2023. Temporal Motifs for Financial Networks: A Study on Mercari JPMC and Venmo Platforms. arXiv."},{"key":"e_1_3_2_2_44_1","volume-title":"Austin R. Benson, and Moses Charikar","author":"Liu Paul","year":"2019","unstructured":"Paul Liu, Austin R. Benson, and Moses Charikar. 2019. Sampling Methods for Counting Temporal Motifs. WSDM."},{"key":"e_1_3_2_2_45_1","unstructured":"Penghang Liu Valerio Guarrasi and A. Erdem Sariyuce. 2021. Temporal Network Motifs: Models Limitations Evaluation. TKDE."},{"key":"e_1_3_2_2_46_1","unstructured":"Penghang Liu and Ahmet Erdem Sariy\u00fcce. 2023. Using Motif Transitions for Temporal Graph Generation. KDD."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622100"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Naoki Masuda and Renaud Lambiotte. 2016. A Guide to Temporal Networks. World Scientific (Europe).","DOI":"10.1142\/q0033"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Michael Mitzenmacher Jakub Pachocki Richard Peng Charalampos Tsourakakis and Shen Chen Xu. 2015. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling. KDD.","DOI":"10.1145\/2783258.2783385"},{"key":"e_1_3_2_2_50_1","volume-title":"Italiano","author":"Oettershagen Lutz","year":"2023","unstructured":"Lutz Oettershagen, Athanasios L. Konstantinidis, and Giuseppe F. Italiano. 2023. Temporal Network Core Decomposition and Community Search. arXiv."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Lutz Oettershagen Nils M. Kriege and Petra Mutzel. 2023. A Higher-Order Temporal H-Index for Evolving Networks. KDD.","DOI":"10.1145\/3580305.3599242"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Lutz Oettershagen and Petra Mutzel. 2020. Efficient Top-k Temporal Closeness Calculation in Temporal Networks. ICDM.","DOI":"10.1109\/ICDM50108.2020.00049"},{"key":"e_1_3_2_2_53_1","volume-title":"Austin R. Benson, and Jure Leskovec","author":"Paranjape Ashwin","year":"2017","unstructured":"Ashwin Paranjape, Austin R. Benson, and Jure Leskovec. 2017. Motifs in Temporal Networks. WSDM."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"crossref","unstructured":"Noujan Pashanasangi and C. Seshadhri. 2021. Faster and Generalized Temporal Triangle Counting via Degeneracy Ordering. KDD.","DOI":"10.1145\/3447548.3467374"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"crossref","unstructured":"Giulia Preti Polina Rozenshtein Aristides Gionis and Yannis Velegrakis. 2021. Discovering Dense Correlated Subgraphs in Dynamic Networks. Advances in Knowledge Discovery and Data Mining.","DOI":"10.1007\/978-3-030-75762-5_32"},{"key":"e_1_3_2_2_56_1","unstructured":"Jiaxi Pu Yanhao Wang Yuchen Li and Xuan Zhou. 2023. Sampling Algorithms for Butterfly Counting on Temporal Bipartite Graphs. arXiv."},{"key":"e_1_3_2_2_57_1","unstructured":"Hongchao Qin Rong-Hua Li Ye Yuan Yongheng Dai and Guoren Wang. 2023. Densest Periodic Subgraph Mining on Large Temporal Graphs. TKDE."},{"key":"e_1_3_2_2_58_1","unstructured":"Hongchao Qin Rong-Hua Li Ye Yuan Guoren Wang Lu Qin and Zhiwei Zhang. 2022. Mining Bursting Core in Large Temporal Graphs. PVLDB."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"crossref","unstructured":"Polina Rozenshtein Francesco Bonchi Aristides Gionis Mauro Sozio and Nikolaj Tatti. 2019. Finding events in temporal networks: segmentation meets densest subgraph discovery. Knowledge and Information Systems.","DOI":"10.1109\/ICDM.2018.00055"},{"key":"e_1_3_2_2_60_1","volume-title":"ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks. WWW.","author":"Santoro Diego","year":"2022","unstructured":"Diego Santoro and Ilie Sarpe. 2022. ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks. WWW."},{"key":"e_1_3_2_2_61_1","volume-title":"Catalyurek","author":"Sariyuce Ahmet Erdem","year":"2015","unstructured":"Ahmet Erdem Sariyuce, C. Seshadhri, Ali Pinar, and Umit V. Catalyurek. 2015. Finding the Hierarchy of Dense Subgraphs using Nucleus Decompositions. WWW."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"crossref","unstructured":"Ilie Sarpe and Fabio Vandin. 2021. odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks. CIKM.","DOI":"10.1145\/3459637.3482459"},{"key":"e_1_3_2_2_63_1","volume-title":"PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts. SDM.","author":"Sarpe Ilie","year":"2021","unstructured":"Ilie Sarpe and Fabio Vandin. 2021. PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts. SDM."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"crossref","unstructured":"Ilie Sarpe Fabio Vandin and Aristides Gionis. 2024. Scalable Temporal Motif Densest Subnetwork Discovery. arXiv.","DOI":"10.1145\/3637528.3671889"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"crossref","unstructured":"Konstantinos Semertzidis Evaggelia Pitoura Evimaria Terzi and Panayiotis Tsaparas. 2018. Finding lasting dense subgraphs. Data Mining and Knowledge Discovery.","DOI":"10.1007\/s10618-018-0602-x"},{"key":"e_1_3_2_2_66_1","unstructured":"Bintao Sun T.-H. Hubert Chan and Mauro Sozio. 2020. Fully Dynamic Approximate k-Core Decomposition in Hypergraphs. TKDD."},{"key":"e_1_3_2_2_67_1","unstructured":"Bintao Sun Maximilien Danisch T-H. Hubert Chan and Mauro Sozio. 2020. KClist: a simple algorithm for finding k-clique densest subgraphs in large graphs. PVLDB."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"crossref","unstructured":"John Tang Mirco Musolesi Cecilia Mascolo and Vito Latora. 2010. Characterising temporal distance and reachability in mobile and online social networks. SIGCOMM.","DOI":"10.1145\/1672308.1672329"},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"crossref","unstructured":"Charalampos Tsourakakis. 2015. The K-clique Densest Subgraph Problem. In WWW.","DOI":"10.1145\/2736277.2741098"},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"crossref","unstructured":"Charalampos E. Tsourakakis. 2014. A Novel Approach to Finding Near-Cliques: The Triangle-Densest Subgraph Problem. arXiv.","DOI":"10.1145\/2736277.2741098"},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"crossref","unstructured":"Alexei Vazquez Bal\u00e1zs R\u00e1cz Andr\u00e1s Luk\u00e1cs and Albert-L\u00e1szl\u00f3 Barab\u00e1si. 2007. Impact of Non-Poissonian Activity Patterns on Spreading Processes. Physical Review Letters.","DOI":"10.1103\/PhysRevLett.98.158702"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"crossref","unstructured":"Jiabing Wang Rongjie Wang Jia Wei Qianli Ma and Guihua Wen. 2020. Finding dense subgraphs with maximum weighted triangle density. Information Sciences.","DOI":"10.1016\/j.ins.2020.06.004"},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"crossref","unstructured":"Jingjing Wang Yanhao Wang Wenjun Jiang Yuchen Li and Kian-Lee Tan. 2020. Efficient Sampling Algorithms for Approximate Temporal Motif Counting. CIKM.","DOI":"10.1145\/3340531.3411862"},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3049278"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"crossref","unstructured":"Qiankun Zhao Yuan Tian Qi He Nuria Oliver Ruoming Jin and Wang-Chien Lee. 2010. Communication motifs. CIKM.","DOI":"10.1145\/1871437.1871694"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"crossref","unstructured":"Ming Zhong Junyong Yang Yuanyuan Zhu Tieyun Qian Mengchi Liu and Jeffrey Xu Yu. 2024. A Unified and Scalable Algorithm Framework of User-Defined Temporal (k X)-Core Query. TKDE.","DOI":"10.1109\/TKDE.2023.3349310"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Barcelona Spain","acronym":"KDD '24"},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671889","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671889","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:15Z","timestamp":1750291455000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":75,"alternative-id":["10.1145\/3637528.3671889","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671889","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}