[1]

Fang Y, Yang Y, Zhang W, Lin X, Cao X. 2020. Effective and efficient community search over large heterogeneous information networks. Proceedings of the VLDB Endowment 13(6):854−867

doi: 10.14778/3380750.3380756
[2]

Fang Y, Wang K, Lin X, Zhang W. 2021. Cohesive subgraph search over big heterogeneous information networks: applications, challenges, and solutions. In Proceedings of the 2021 International Conference on Management of Data. June 20–25, 2021, Virtual Event, China. New York, USA: ACM. pp. 2829–2838 doi: 10.1145/3448016.345753

[3]

Wang Y, Gu C, Xu X, Zeng X, Ke X, et al. 2024. Efficient and effective (k, p)-core-based community search over attributed heterogeneous information networks. Information Sciences, 661:120076

doi: 10.1016/j.ins.2023.120076
[4]

Liu Q, Zhu Y, Zhao M, Huang X, Xu J, et al. 2020. Vac: Vertex-centric attributed community search. 2020 IEEE 36th International Conference on Data Engineering (ICDE), april 20–24, 2020. Dallas, TX, USA. USA: IEEE. pp. 937–948 doi: 10.1109/ICDE48307.2020.00086.

[5]

Zhang Z, Huang X, Xu J, Choi B, Shang Z. 2019. Keyword-centric community search. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). April 8–11, 2019. Macao, China. USA: IEEE. pp. 422–433 doi: 10.1109/ICDE.2019.00045.

[6]

Chen L, Liu C, Zhou R, Li J, Yang X, et al. 2018. Maximum co-located community search in large scale social networks. Proceedings of the VLDB Endowment 11(10):1233−1246

doi: 10.14778/3231751.3231755
[7]

Zhang F, Zhang Y, Qin L, Zhang W, Lin X. 2017. Finding critical users for social network engagement: the collapsed k-core problem. Proceedings of the AAAI Conference on Artificial Intelligence 31(1):245−251

doi: 10.1609/aaai.v31i1.10482
[8]

Ley M. 2002. The dblp computer science bibliography: evolution, research issues, perspectives. In String Processing and Information Retrieval. Berlin, Heidelberg: Springer. pp. 1–10 doi: 10.1007/3-540-45735-6_1

[9]

Tang J. 2016. Aminer: Toward understanding big scholar data. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. San Francisco California USA . New York, USA: ACM. pp. 467 doi: 10.1145/2835776.2835849

[10]

Mendes P, Jakob M, Bizer C. 2012. Dbpedia: a multilingual cross-domain knowledge base. Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012). 21−27 May 2012, Istanbul, Turkey. European Language Resources Association (ELRA). pp. 1813−1817 doi: 10.63317/4b94v6njihis

[11]

Hoffart J, Suchanek FM, Berberich K, Weikum G. 2013. Klaus Berberich, and Gerhard Weikum. YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence 194:28−61

doi: 10.1016/j.artint.2012.06.001
[12]

Yao K, Chang L . 2021. Efficient size-bounded community search over large networks. Proceedings of the VLDB Endowment 14(8):1441−1453

doi: 10.14778/3457390.3457407
[13]

Ye J, Zhu Y, Chen L. 2023. Top-r keyword-based community search in attributed graphs. 2023 IEEE 39th International Conference on Data Engineering (ICDE). April 3–7, 2023, Anaheim, CA, USA. USA: IEEE. pp. 1652–1664 doi:10.1109/ICDE55515.2023.00130.

[14]

Wang Y, Gou X, Xu X, Geng Y, Ke X, et al. 2024. Scalable community search over large-scale graphs based on graph transformer. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 14–18 July 2024, Washington D.C., USA. New York, USA: ACM. pp. 1680–1690 doi: 10.1145/3626772.3657771

[15]

Jiang Y, Fang Y, Ma C, Cao X, Li C . 2022. Effective community search over large star-schema heterogeneous information networks. Proceedings of the VLDB Endowment 15(11):2307−2320

doi: 10.14778/3551793.3551795
[16]

Xu X, Liu J, Wang Y, Ke X. 2022. Academic expert finding via $(k, {\cal{P}})$-core based embedding over heterogeneous graphs. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 9–12 May 2022, Kuala Lumpur, Malaysia. USA: IEEE. pp. 338–351doi: 10.1109/icde53745.2022.00030

[17]

Sozio M, Gionis A. 2010. The community-search problem and how to plan a successful cocktail party. KDD'10: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25–28, 2010, Washington D.C., USA. New York, NY, USA: Association for Computing Machinery. pp. 939–948 doi: 10.1145/1835804.1835923

[18]

Wang Y, Liu J, Xu X, Ke X, Wu T, et al. 2023. Efficient and effective academic expert finding on heterogeneous graphs through (k, ${\cal{P}}$)-core based embedding. ACM Transactions on Knowledge Discovery from Data 17(6):1−35

doi: 10.1145/3578365
[19]

Dudley JT, Deshpande T, Butte AJ. 2011. Exploiting drug-disease relationships for computational drug repositioning. Briefings in Bioinformatics 12(4):303−311

doi: 10.1093/bib/bbr013
[20]

Pesántez-Cabrera P, Kalyanaraman A. 2019. Efficient detection of communities in biological bipartite networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 16(1):258−271

doi: 10.1109/TCBB.2017.2765319
[21]

Guo Z, Xia L, Yu Y, Ao T, Huang C. 2024. LightRAG: simple and fast retrieval-augmented generation. arXiv Preprint

doi: 10.48550/arXiv.2410.05779
[22]

Edge D, Trinh H, Cheng N, Bradley J, Chao A, et al. 2024. From local to global: a graph rag approach to query-focused summarization. arXiv Preprint

doi: 10.48550/arXiv.2404.16130
[23]

Wang S, Fang Y, Zhou Y, Liu X, Ma Y. 2026. ArchRAG: attributed community-based hierarchical retrieval-augmented generation. Proceedings of the AAAI Conference on Artificial Intelligence 40(19):15868−15876

doi: 10.1609/aaai.v40i19.38619
[24]

YLiu Y, Guo F, Xu B, Bao P, Shen H, et al. 2023. Significant-attributed community search in heterogeneous information networks. arXiv Preprint

doi: 10.48550/arXiv.2308.13244
[25]

Yang Y, Fang Y, Lin X, Zhang W. 2020. Effective and efficient truss computation over large heterogeneous information networks. 2020 IEEE 36th International Conference on Data Engineering (ICDE). April 20–24, 2020. Dallas, TX, USA . USA: IEEE. pp. 901–912 doi: 10.1109/icde48307.2020.00083

[26]

Barbieri N, Bonchi F, Galimberti E, Gullo F. 2015. 2015. Efficient and effective community search. Data Mining and Knowledge Discovery 29(5):1406−1433

doi: 10.1007/s10618-015-0422-1
[27]

Cui W, Xiao Y, Wang H, Wang W. 2014. Local search of communities in large graphs. SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, June 22–27, 2014, Snowbird, Utah, USA. New York, NY, USA: Association for Computing Machinery. pp. 991–1002 doi: 10.1145/2588555.2612179

[28]

Huang X, Cheng H, Qin L, Tian W, Yu JX. 2014. Querying k-truss community in large and dynamic graphs. SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, June 22–27, 2014, Snowbird, Utah, USA. New York, NY, USA: Association for Computing Machinery. pp. 1311–1322 doi: 10.1145/2588555.2610495

[29]

Huang X, Lakshmanan LVS, Yu JX, Cheng H . 2015. Approximate Closest Community Search in Networks. PVLDB 9(4):276−287

doi: 10.14778/2856318.2856323
[30]

Wang Y, Khan A, Wu T, Jin J, Yan H. 2020. Semantic guided and response times bounded top-k similarity search over knowledge graphs. 2020 IEEE 36th International Conference on Data Engineering (ICDE), April 2020, Dallas, TX, USA. USA: IEEE. pp. 445–456 doi: 10.1109/icde48307.2020.00045

[31]

Chen L, Liu C, Liao K, Li J, Zhou R. 2019. Contextual community search over large social networks. 2019 IEEE 35th International Conference on Data Engineering (ICDE), April 8-11, 2019. Macao, China. USA: IEEE. pp. 88–99 doi: 10.1109/ICDE.2019.00017

[32]

Campana P, Varese F. 2022. Studying organized crime networks: data sources, boundaries and the limits of structural measures. Social Networks 69:149−159

doi: 10.1016/j.socnet.2020.03.002
[33]

Chattoe E, Hamill H. 2005. It’s not who you know—it’s what you know about people you don’t know that counts: extending the analysis of crime groups as social networks. The British Journal of Criminology 45(6):860−876

doi: 10.1093/bjc/azi051
[34]

Wang T, Rudin C, Wagner D, Sevieri R. 2013. Learning to detect patterns of crime. In Machine Learning and Knowledge Discovery in Databases, eds. Blockeel H, Kersting K, Nijssen S, Železný F. Berlin, Heidelberg: Springer. pp. 515–530 doi: 10.1007/978-3-642-40994-3_33

[35]

Wang Y, Khan A, Xu X, Jin J, Hong Q, et al. 2022. Aggregate queries on knowledge graphs: Fast approximation with semantic-aware sampling. 2022 IEEE 38th International Conference on Data Engineering (ICDE). May 9–12, 2022, Kuala Lumpur, Malaysia. USA: IEEE. pp. 2914–2927 doi: 10.1109/icde53745.2022.00263

[36]

Zhang Z, Huang X, Xu J, Choi B, Shang Z. 2019. Keyword-centric community search. 2019 IEEE 35th International Conference on Data Engineering (ICDE). April 8–11, 2019 Macao, China. USA: IEEE. pp. 422–433 doi: 10.1109/ICDE.2019.00045

[37]

Sun Z, Deng ZH, Nie JY, Tang J. 2019. Rotate: knowledge graph embedding by relational rotation in complex space. arXiv Preprint

doi: 10.48550/arXiv.1902.10197
[38]

Huang X, Zhang J, Li D, Li P. 2019. Knowledge graph embedding based question answering. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM '19, New York, NY, USA, 2019. USA: Association for Computing Machinery. pp. 105–113 doi: 10.1145/3289600.3290956

[39]

Batagelj V, Zaveršnik M. 2003. An O(m) algorithm for cores decomposition of networks. arXiv Preprint

doi: 10.48550/arXiv.cs/0310049
[40]

Anonymous GitHub. 2025. Top-r semantically important community search on semantic-rich heterogeneous graphs (r-SICS). https://anonymous.4open.science/r/rSICS-42B6

[41]

Vrandečić D, Krötzsch M. 2014. Wikidata: a free collaborative knowledgebase. Communications of the ACM 57(10):78−85

doi: 10.1145/2629489
[42]

Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD '08, 2008, New York, NY, USA. USA: Association for Computing Machinery. pp. 1247–1250 doi: 10.1145/1376616.1376746

[43]

Rebele T, Suchanek F, Hoffart J, Biega J, Kuzey E, et al. 2016. YAGO: a multilingual knowledge base from wikipedia, wordnet, and geonames. In The Semantic Web – ISWC 2016, eds. Groth P, Simperl E, Gray A, Sabou M, Krötzsch M, et al. Cham: Springer. pages 177–185 doi: 10.1007/978-3-319-46547-0_19

[44]

Sun Y, Han J, Yan X, Yu PS, Wu T . 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment 4(11):992−1003

doi: 10.14778/3402707.3402736
[45]

Meng C, Cheng R, Maniu S, Senellart P, Zhang W. 2015. Discovering meta-paths in large heterogeneous information networks. WWW '15: Proceedings of the 24th International Conference on World Wide Web, May 18–22, 2015, Florence, Italy. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. pp. 754–764 doi: 10.1145/2736277.2741123

[46]

Shi C, Li Y, Zhang J, Sun Y, Yu PS. 2017. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering 29(1):17−37

doi: 10.1109/TKDE.2016.2598561
[47]

Fang Y, Wang Z, Cheng R, Wang H, Hu J. 2019. Effective and efficient community search over large directed graphs. IEEE Transactions on Knowledge and Data Engineering 31(11):2093−2107

doi: 10.1109/TKDE.2018.2872982
[48]

Liu Q, Zhao M, Huang X, Xu J, Gao Y. 2020. Truss-based community search over large directed graphs. SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. New York, NY, USA: Association for Computing Machinery. pp. 2183–2197 doi: 10.1145/3318464.3380587

[49]

Cui W, Xiao Y, Wang H, Lu Y, Wang W. 2013. Online search of overlapping communities. SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, June 22–27, 2013, New York, USA. New York, NY, USA: Association for Computing Machinery. pp. 277–288 doi: 10.1145/2463676.2463722

[50]

Yuan L, Qin L, Zhang W, Chang L, Yang J . 2018. Index-based densest clique percolation community search in networks. IEEE Transactions on Knowledge and Data Engineering 30(5):922−935

doi: 10.1109/TKDE.2017.2783933
[51]

Zheng D, Liu J, Li RH, Aslay Ç, Chen YC, et al. 2017. Querying intimate-core groups in weighted graphs. Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, 30 January – 1 February, 2017. Piscataway, NJ, USA: IEEE. pp. 156–163 doi: 10.1109/ICSC.2017.80

[52]

Li L, Zhao Y, Luo S, Wang G, Wang Z. 2023. Efficient community search in edge-attributed graphs. IEEE Transactions on Knowledge and Data Engineering 35(10):10790−10806

doi: 10.1109/TKDE.2023.3267550
[53]

Habib WMA, Mokhtar HMO, El-Sharkawi ME. 2022. Discovering top-weighted k-truss communities in large graphs. Journal of Big Data 9(1):36

doi: 10.1186/s40537-022-00588-1
[54]

Zhou Y, Fang Y, Luo W, Ye Y. 2023. Influential community search over large heterogeneous information networks. Proceedings of the VLDB Endowment 16(8):2047−2060

doi: 10.14778/3594512.3594532