COMET: An Interactive Framework for Efficient and Effective Community Search via Active Learning

Published Online:https://doi.org/10.1287/ijoc.2024.0834

References

  • Akbas E, Zhao P (2017) Truss-based community search: A truss-equivalence based indexing approach. Proc. VLDB Endow 10(11):1298–1309.CrossrefGoogle Scholar
  • Andersen R, Chung FRK, Lang KJ (2006) Local graph partitioning using pagerank vectors. 47th Annual IEEE Sympos. Foundations Comput. Sci. (IEEE Computer Society, Washington, DC), 475–486.Google Scholar
  • Andersen R, Chung F, Lang K (2007) Local partitioning for directed graphs using pagerank. Bonato A, Chung FRK, eds. Algorithms and Models for the Web-Graph (Springer, Berlin), 166–178.CrossrefGoogle Scholar
  • Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J. Statist. Mechanics Theory Experiment 2008(10):P10008.CrossrefGoogle Scholar
  • Bo D, Wang X, Shi C, Zhu M, Lu E, Cui P (2020) Structural deep clustering network. Huang Y, King I, Liu T-Y, van Steen M, eds. WWW’20: Proc. Web Conf. (ACM, New York), 1400–1410.Google Scholar
  • Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput. Networks ISDN Systems 30(1–7):107–117.CrossrefGoogle Scholar
  • Cai B, Wang Y, Zeng L, Hu Y, Li H (2020) Edge classification based on convolutional neural networks for community detection in complex network. Phys. A Statist. MechanicsIts Appl. 556:124826.CrossrefGoogle Scholar
  • Chamberlain BP, Shirobokov S, Rossi E, Frasca F, Markovich T, Hammerla NY, Bronstein MM, Hansmire M (2023) Graph neural networks for link prediction with subgraph sketching. 11th Internat. Conf. Learn. Representations, ICLR (OpenReview.net).Google Scholar
  • Chang L, Lin X, Qin L, Yu JX, Zhang W (2015) Index-based optimal algorithms for computing steiner components with maximum connectivity. Sellis TK, Davidson SB, Ives ZG, eds. Proc. 2015 ACM SIGMOD Internat. Conf. Management Data (ACM, New York), 459–474.Google Scholar
  • Chen T, Tsourakakis CE (2022) AntiBenford subgraphs: Unsupervised anomaly detection in financial networks. Zhang A, Rangwala H, eds. KDD’22: 28th ACM SIGKDD Conf. Knowledge Discovery Data Mining (ACM, New York), 2762–2770Google Scholar
  • Cheng J, Wang Q, Tao Z, Xie D-Y, Gao Q (2021) Multi-view attribute graph convolution networks for clustering. Bessiere C, ed. Proc. 29th Internat. Joint Conf. Artificial Intelligence (ijcai.org), 2973–2979.Google Scholar
  • Condon A, Karp RM (2001) Algorithms for graph partitioning on the planted partition model. Random Structures Algorithms 18(2):116–140.CrossrefGoogle Scholar
  • Cui W, Xiao Y, Wang H, Wang W (2014) Local search of communities in large graphs. ACM SIGMOD Internat. Conf. Management Data (ACM, New York), 991–1002.Google Scholar
  • De Santo A, Galli A, Moscato V, Sperlì G (2021) A deep learning approach for semi-supervised community detection in online social networks. Knowledge-Based Systems 229:107345.CrossrefGoogle Scholar
  • Des Mesnards NG, Hunter DS, el Hjouji Z, Zaman T (2022) Detecting bots and assessing their impact in social networks. Oper. Res. 70(1):1–22.LinkGoogle Scholar
  • Dou Y, Liu Z, Sun L, Deng Y, Peng H, Yu PS (2020) Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. d’Aquin M, Dietze S, Hauff C, Curry E, Cudré-Mauroux P, eds. Proc. 29th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 315–324.Google Scholar
  • Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. Internat. Conf. Knowledge Discovery Data Mining (AAAI Press, Palo Alto, CA), 226–231.Google Scholar
  • Fan W, Ma Y, Li Q, He Y, Zhao YE, Tang J, Yin D (2019) Graph neural networks for social recommendation. Liu L, White RW, Mantrach A, Silvestri F, McAuley JJ, Baeza-Yates R, Zia L, eds. World Wide Web Conf. (ACM, New York), 417–426.Google Scholar
  • Feng S, Tan Z, Wan H, Wang N, Chen Z, Zhang B, Zheng Q, et al. (2022) TwiBot-22: Towards graph-based twitter bot detection. Adv. Neural Inform. Processing Systems, vol. 35 (Curran Associates Inc., New York), 35254–35269.Google Scholar
  • Fu X, Zhang J, Meng Z, King I (2020) MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding. Huang Y, King I, Liu T-Y, van Steen M, eds. Proc. Web Conf. (ACM, New York), 2331–2341.Google Scholar
  • Gao J, Chen J, Li Z, Zhang J (2021) ICS-GNN: Lightweight interactive community search via graph neural network. Proc. VLDB Endow 14(6):1006–1018.CrossrefGoogle Scholar
  • Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc. Natl Acad. Sci. USA 99(12):7821–7826.CrossrefGoogle Scholar
  • Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. Krishnapuram B, Shah M, Smola AJ, Aggarwal CC, Shen D, Rastogi R, eds. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 855–864.Google Scholar
  • Guo T, Peng S, Li Y, Zhou M, Truong TK (2023) Community-based social recommendation under local differential privacy protection. Inform. Sci. 639:119002.CrossrefGoogle Scholar
  • Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 1024–1034.Google Scholar
  • He Z, Li C, Zhou F, Yang Y (2021) Rumor detection on social media with event augmentations. Diaz F, Shah C, Suel T, Castells P, Jones R, Sakai T, eds. SIGIR ’21: 44th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 2020–2024.Google Scholar
  • Holland PW, Laskey KB, Leinhardt S (1983) Stochastic blockmodels: First steps. Soc. Networks 5(2):109–137.CrossrefGoogle Scholar
  • Huang Z, Zeng DD, Chen H (2007) Analyzing consumer-product graphs: Empirical findings and applications in recommender systems. Management Sci. 53(7):1146–1164.LinkGoogle Scholar
  • Huang X, Lakshmanan LVS, Yu JX, Cheng H (2015) Approximate closest community search in networks. Proc. VLDB Endow 9(4):276–287.CrossrefGoogle Scholar
  • Huang X, Cheng H, Qin L, Tian W, Yu JX (2014) Querying k-truss community in large and dynamic graphs. Dyreson CE, Li F, Tamer Özsu M, eds. Internat. Conf. Management Data (ACM, New York), 1311–1322.Google Scholar
  • Jeh G, Widom J (2002) SimRank: A measure of structural-context similarity. Proc. 8th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 538–543.Google Scholar
  • Jerrum M, Sinclair A (1988) Conductance and the rapid mixing property for Markov chains: The approximation of the permanent resolved (preliminary version). Simon J, ed. Proc. 20th Annual ACM Sympos. Theory Comput. (ACM, New York), 235–244.Google Scholar
  • Jiang Y, Rong Y, Cheng H, Huang X, Zhao K, Huang J (2022) Query driven-graph neural networks for community search: From non-attributed, attributed, to interactive attributed. Proc. VLDB Endow 15(6):1243–1255.CrossrefGoogle Scholar
  • Kannan R, Vempala S, Vetta A (2004) On clusterings: Good, bad and spectral. J. ACM 51(3):497–515.CrossrefGoogle Scholar
  • Karypis G, Kumar V (1998) Multilevelk-way partitioning scheme for irregular graphs. J. Parallel Distributed Comput. 48(1):96–129.CrossrefGoogle Scholar
  • Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell System Tech. J. 49(2):291–307.CrossrefGoogle Scholar
  • Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. 5th Internat. Conf. Learn. Representations (OpenReview.net).Google Scholar
  • Kloumann IM, Kleinberg JM (2014) Community membership identification from small seed sets. Macskassy SA, Perlich C, Leskovec J, Wang W, Ghani R, eds. 20th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1366–1375.Google Scholar
  • Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4):046110.CrossrefGoogle Scholar
  • Leskovec J, Krevl A (2014) SNAP Data sets: Stanford large network data set collection. Accessed August 25, 2025, http://snap.stanford.edu/data.Google Scholar
  • Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. Rappa M, Jones P, Freire J, Chakrabarti S, eds. Proc. 19th Internat. Conf. World Wide Web (ACM, New York), 641–650.Google Scholar
  • Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: Densification and shrinking diameters. ACM Trans. Knowledge Discovery Data 1(1):2.CrossrefGoogle Scholar
  • Li J, Liu C, Yu JX, Chen Y, Sellis T, Culpepper JS (2016) Personalized influential topic search via social network summarization. IEEE Trans. Knowledge Data Engrg. 28(7):1820–1834.CrossrefGoogle Scholar
  • Li L, Luo S, Zhao Y, Shan C, Wang Z, Qin L (2023) COCLEP: Contrastive learning-based semi-supervised community search. 39th IEEE Internat. Conf. Data Engrg. ICDE 2023 (IEEE, Piscataway, NJ), 2483–2495.Google Scholar
  • Luo F, Wang JZ, Promislow E (2008) Exploring local community structures in large networks. Web Intelligence Agent Systems 6(4):387–400.CrossrefGoogle Scholar
  • Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations: Can geographic isolation explain this unique trait? Behav. Ecology Sociobiology 54:396–405.CrossrefGoogle Scholar
  • Ma YL, Yuan Y, Zhu FD, Wang GR, Xiao J, Wang JZ (2019) Who should be invited to my party: A size-constrained k-core problem in social networks. J. Comput. Sci. Tech. 34:170–184.CrossrefGoogle Scholar
  • Moghaddass R, Guan Y (2022) Optimal frameworks for detecting anomalies in sensor-intensive heterogeneous networks. INFORMS J. Comput. 34(5):2583–2610.LinkGoogle Scholar
  • Newman ME (2004) Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6):066133.CrossrefGoogle Scholar
  • Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys. Rev. E 69(2):026113.CrossrefGoogle Scholar
  • Nussbaum R (2003) Notes on the second eigenvalue of the Google matrix. Preprint, submitted July 3, https://arxiv.org/abs/math/0307056.Google Scholar
  • Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, Stanford, CA.Google Scholar
  • Park C, Kim D, Han J, Yu H (2020) Unsupervised attributed multiplex network embedding. 34th AAAI Conf. Artificial Intelligence, vol. 34 (AAAI Press, Palo Alto, CA), 5371–5378.Google Scholar
  • Raschid L, Sayyadi H, Hristidis V (2019) Learning to rank in entity relationship graphs. INFORMS J. Comput. 31(4):671–688.LinkGoogle Scholar
  • Sozio M, Gionis A (2010) The community-search problem and how to plan a successful cocktail party. Rao B, Krishnapuram B, Tomkins A, Yang Q, eds. Proc. 16th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 939–948.Google Scholar
  • Spielman DA, Teng SH (2004) Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. Babai L, ed. Proc. 36th Annual ACM Sympos. Theory Comput. (ACM, New York), 81–90.Google Scholar
  • Timonina-Farkas A, Seifert RW (2023) Information retrieval under network uncertainty: Robust internet ranking. Oper. Res. 71(6):2328–2351.LinkGoogle Scholar
  • Tsourakakis CE (2015) The K-clique densest subgraph problem. Gangemi A, Leonardi S, Pancones A, eds. Proc. 24th Internat. Conf. World Wide Web (ACM, New York), 1122–1132.Google Scholar
  • Van Vlasselaer V, Eliassi-Rad T, Akoglu L, Snoeck M, Baesens B (2017) Gotcha! Network-based fraud detection for social security fraud. Management Sci. 63(9):3090–3110.LinkGoogle Scholar
  • Velickovic P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. 6th Internat. Conf. Learn. Representations ICLR (OpenReview.net).Google Scholar
  • Wang X, Li J, Yang L, Mi H (2021) Unsupervised learning for community detection in attributed networks based on graph convolutional network. Neurocomputing 456:147–155.CrossrefGoogle Scholar
  • Wang D, Qi Y, Lin J, Cui P, Jia Q, Wang Z, Fang Y, Yu Q, Zhou J, Yang S (2019) A semi-supervised graph attentive network for financial fraud detection. Wang J, Shim K, Wu X, eds. 2019 IEEE Internat. Conf. Data Mining, ICDM (IEEE, Piscataway, NJ), 598–607.Google Scholar
  • Wu Z, Liu G, Wu J, Tan Y (2024) Are neighbors alike? A semisupervised probabilistic collaborative learning model for online review spammers detection. Inform. Systems Res. 35(4):1565–1585.LinkGoogle Scholar
  • Yang D, Rosso P, Li B, Cudre-Mauroux P (2019) NodeSketch: Highly-efficient graph embeddings via recursive sketching. Teredesai A, Kumar V, Li Y, Rosales R, Terzi E, Karypis G, eds. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1162–1172.Google Scholar
  • Yin H, Benson AR, Leskovec J, Gleich DF (2017) Local higher-order graph clustering. Proc. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 555–564.Google Scholar
  • Yuan L, Qin L, Zhang W, Chang L, Yang J (2017) Index-based densest clique percolation community search in networks. IEEE Trans. Knowledge Data Engrg. 30(5):922–935.CrossrefGoogle Scholar
  • Zachary WW (1977) An information flow model for conflict and fission in small groups. J. Anthropological Res. 33(4):452–473.CrossrefGoogle Scholar
  • Zeng M, Zhang F, Wu FX, Li Y, Wang J, Li M (2020) Protein–protein interaction site prediction through combining local and global features with deep neural networks. Bioinformatics 36(4):1114–1120.CrossrefGoogle Scholar
  • Zhang J, Tang J, Ma C, Tong H, Jing Y, Li J (2015) Panther: Fast top-k similarity search on large networks. Cao L, Zhang C, Joachims T, Webb GI, Margineantu DD, Williams G, eds. Proc. 21th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1445–1454.Google Scholar
  • Zhang T, Xiong Y, Zhang J, Zhang Y, Jiao Y, Zhu Y (2020) CommDGI: Community detection oriented deep graph infomax. d’Aquin M, Dietze S, Hauff C, Curry E, Cudré-Mauroux P, eds. CIKM ’20: 29th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 1843–1852.Google Scholar
  • Zheng X, Cai Z, Luo G, Tian L, Bai X (2019) Privacy-preserved community discovery in online social networks. Future Generation Comput. Systems 93:1002–1009.CrossrefGoogle Scholar
  • Zhou Y, Li J, Hao JK, Glover F (2024) Detecting critical nodes in sparse graphs via “reduce-solve-combine” memetic search. INFORMS J. Comput. 36(1):39–60.LinkGoogle Scholar
  • Zhou J, Wang K, Wang J, Zhang K, Lin X (2025) COMET: An interactive framework for efficient and effective community search via active learning. https://doi.org/10.1287/ijoc.2024.0834.cd, https://github.com/INFORMSJoC/2024.0834.Google Scholar
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