{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T07:14:49Z","timestamp":1783667689589,"version":"3.55.0"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["The VLDB Journal"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s00778-026-00990-8","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:38:24Z","timestamp":1782931104000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["$$\\textsf{IACS}^{+}$$: Inductive Attributed Community Search via Learning across Graphs"],"prefix":"10.1007","volume":"35","author":[{"given":"Ao","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuheng","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangfei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey","family":"Xu Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoli","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyu","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoren","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"990_CR1","unstructured":"Pytorch. https:\/\/github.com\/pytorch\/pytorch"},{"key":"990_CR2","unstructured":"Pytorch Geometric. https:\/\/github.com\/rusty1s\/pytorch_geometric"},{"key":"990_CR3","doi-asserted-by":"crossref","unstructured":"Bai, Y., Ding, H., Bian, S., Chen, T., Sun, Y., Wang, W.: Simgnn: A neural network approach to fast graph similarity computation. In Proceedings of the twelfth ACM international conference on web search and data mining, pages 384\u2013392, (2019)","DOI":"10.1145\/3289600.3290967"},{"key":"990_CR4","doi-asserted-by":"crossref","unstructured":"Bai, Y., Ding, H., Qiao, Y., Marinovic, A., Gu, K., Chen, T., Sun, Y., Wang, W.: Unsupervised inductive graph-level representation learning via graph-graph proximity. In Proc. IJCAI, pages 1988\u20131994. ijcai.org, (2019)","DOI":"10.24963\/ijcai.2019\/275"},{"key":"990_CR5","unstructured":"Bai, Y., Xu, D., Sun, Y., Wang, W.: Glsearch: Maximum common subgraph detection via learning to search. In International Conference on Machine Learning, pages 588\u2013598. PMLR, (2021)"},{"key":"990_CR6","doi-asserted-by":"crossref","unstructured":"Bateni, P., Goyal, R., Masrani, V., Wood, F., Sigal, L.: Improved few-shot visual classification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 14493\u201314502, (2020)","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"990_CR7","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1609\/aaai.v34i01.5393","volume":"34","author":"T Bian","year":"2020","unstructured":"Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., Huang, J.: Rumor detection on social media with bi-directional graph convolutional networks. In Proceedings of the AAAI conference on artificial intelligence 34, 549\u2013556 (2020)","journal-title":"In Proceedings of the AAAI conference on artificial intelligence"},{"key":"990_CR8","unstructured":"Brockschmidt, M.: Gnn-film: Graph neural networks with feature-wise linear modulation. In International Conference on Machine Learning, pages 1144\u20131152. PMLR, (2020)"},{"key":"990_CR9","first-page":"130","volume":"24","author":"Q Cappart","year":"2023","unstructured":"Cappart, Q., Ch\u00e9telat, D., Khalil, E.B., Lodi, A., Morris, C., Velickovic, P.: Combinatorial optimization and reasoning with graph neural networks. J. Mach. Learn. Res. 24, 130\u20131 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"990_CR10","doi-asserted-by":"crossref","unstructured":"Chen, G., Guo, F., Wang, Y., Liu, Y., Yu, P., Shen, H., Cheng, X.: Fcs-hgnn: Flexible multi-type community search in heterogeneous information networks. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pages 207\u2013217, (2024)","DOI":"10.1145\/3627673.3679696"},{"key":"990_CR11","unstructured":"Chen, J., Gao, K., Li, G., He, K.: Nagphormer: A tokenized graph transformer for node classification in large graphs. In: Proc. ICLR 2023. OpenReview.net, (2023)"},{"issue":"10","key":"990_CR12","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.14778\/3603581.3603595","volume":"16","author":"J Chen","year":"2023","unstructured":"Chen, J., Xia, Y., Gao, J.: Communityaf: An example-based community search method via autoregressive flow. Proceedings of the VLDB Endowment 16(10), 2565\u20132577 (2023)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"990_CR13","unstructured":"Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In Proc. ICML 2020, volume 119 of Proceedings of Machine Learning Research, pages 1725\u20131735. PMLR, (2020)"},{"key":"990_CR14","unstructured":"Chen, Z., Li, X., Bruna, J.: Supervised community detection with line graph neural networks, (2017). arXiv:1705.08415 arXiv preprint"},{"key":"990_CR15","doi-asserted-by":"crossref","unstructured":"Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.-J.: Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 257\u2013266, (2019)","DOI":"10.1145\/3292500.3330925"},{"key":"990_CR16","first-page":"798","volume":"2021","author":"D Daza","year":"2021","unstructured":"Daza, D., Cochez, M., Groth, P.: Inductive entity representations from text via link prediction. In Proceedings of the Web Conference 2021, 798\u2013808 (2021)","journal-title":"In Proceedings of the Web Conference"},{"issue":"5","key":"990_CR17","doi-asserted-by":"publisher","first-page":"730","DOI":"10.14778\/3446095.3446097","volume":"14","author":"CT Duong","year":"2021","unstructured":"Duong, C.T., Hoang, T.D., Yin, H., Weidlich, M., Nguyen, Q.V.H., Aberer, K.: Efficient streaming subgraph isomorphism with graph neural networks. Proceedings of the VLDB Endowment 14(5), 730\u2013742 (2021)","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"6394","key":"990_CR18","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1126\/science.aar6170","volume":"360","author":"SA Eslami","year":"2018","unstructured":"Eslami, S.A., Rezende, D.J., Besse, F., Viola, F., Morcos, A.S., Garnelo, M., Ruderman, A., Rusu, A.A., Danihelka, I., Gregor, K., et al.: Neural scene representation and rendering. Science 360(6394), 1204\u20131210 (2018)","journal-title":"Science"},{"key":"990_CR19","doi-asserted-by":"crossref","unstructured":"Fang, S., Zhao, K., Li, G., Yu, J.X.: Community search: a meta-learning approach. In 2023 IEEE 39th International Conference on Data Engineering (ICDE), pages 2358\u20132371. IEEE, (2023)","DOI":"10.1109\/ICDE55515.2023.00182"},{"key":"990_CR20","doi-asserted-by":"crossref","unstructured":"Fang, S., Zhao, K., Rong, Y., Li, Z., Yu, J.X.: All-in-one: Heterogeneous interaction modeling for cold-start rating prediction, (2024). arXiv:2403.17740 arXiv preprint","DOI":"10.1109\/ICDE65448.2025.00119"},{"issue":"10","key":"990_CR21","doi-asserted-by":"publisher","first-page":"2576","DOI":"10.14778\/3675034.3675048","volume":"17","author":"S Fang","year":"2024","unstructured":"Fang, S., Zhao, K., Rong, Y., Li, Z., Yu, J.X.: Inductive attributed community search: to learn communities across graphs. Proc. VLDB Endow. 17(10), 2576\u20132589 (2024)","journal-title":"Proc. VLDB Endow."},{"key":"990_CR22","doi-asserted-by":"crossref","unstructured":"Fang, Y., Cheng, C., Luo, S., Hu, J.: Effective community search for large attributed graphs. In: Proceedings of the VLDB Endowment, (2016)","DOI":"10.14778\/2994509.2994538"},{"key":"990_CR23","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126\u20131135. PMLR, (2017)"},{"issue":"6","key":"990_CR24","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.14778\/3447689.3447704","volume":"14","author":"J Gao","year":"2021","unstructured":"Gao, J., Chen, J., Li, Z., Zhang, J.: Ics-gnn: lightweight interactive community search via graph neural network. Proceedings of the VLDB Endowment 14(6), 1006\u20131018 (2021)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"990_CR25","unstructured":"Gasse, M., Ch\u00e9telat, D., Ferroni, N., Charlin, L., Lodi, A.: Exact combinatorial optimization with graph convolutional neural networks. In: Advances in neural information processing systems, vol. 32, (2019)"},{"key":"990_CR26","doi-asserted-by":"crossref","unstructured":"Guo, F., Yuan, Y., Wang, G., Zhao, X., Sun, H.: Multi-attributed community search in road-social networks. In 2021 IEEE 37th International Conference on Data Engineering (ICDE), pages 109\u2013120. IEEE, (2021)","DOI":"10.1109\/ICDE51399.2021.00017"},{"key":"990_CR27","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in neural information processing systems, vol. 30, (2017)"},{"key":"990_CR28","doi-asserted-by":"crossref","unstructured":"Hao, Y., Cao, X., Fang, Y., Xie, X., Wang, S.: Inductive link prediction for nodes having only attribute information. In Proc. IJCAI, pages 1209\u20131215. ijcai.org, (2020)","DOI":"10.24963\/ijcai.2020\/168"},{"key":"990_CR29","unstructured":"Huang, W., Zhang, T., Rong, Y., Huang, J.: Adaptive sampling towards fast graph representation learning. Advances in neural information processing systems, 31, (2018)"},{"key":"990_CR30","doi-asserted-by":"crossref","unstructured":"Huang, X., Lakshmanan, L.V., Xu, J.: Community search over big graphs: Models, algorithms, and opportunities. In 2017 IEEE 33rd international conference on data engineering (ICDE), pages 1451\u20131454. IEEE, (2017)","DOI":"10.1109\/ICDE.2017.211"},{"issue":"9","key":"990_CR31","doi-asserted-by":"publisher","first-page":"949","DOI":"10.14778\/3099622.3099626","volume":"10","author":"X Huang","year":"2017","unstructured":"Huang, X., Lakshmanan, L.V.S.: Attribute-driven community search. Proc. VLDB Endow. 10(9), 949\u2013960 (2017)","journal-title":"Proc. VLDB Endow."},{"issue":"4","key":"990_CR32","doi-asserted-by":"publisher","first-page":"276","DOI":"10.14778\/2856318.2856323","volume":"9","author":"X Huang","year":"2015","unstructured":"Huang, X., Lakshmanan, L.V.S., Yu, J.X., Cheng, H.: Approximate closest community search in networks. Proc. VLDB Endow. 9(4), 276\u2013287 (2015)","journal-title":"Proc. VLDB Endow."},{"issue":"6","key":"990_CR33","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.14778\/3514061.3514070","volume":"15","author":"Y Jiang","year":"2022","unstructured":"Jiang, Y., Rong, Y., Cheng, H., Huang, X., Zhao, K., Huang, J.: Query driven-graph neural networks for community search: from non-attributed, attributed, to interactive attributed. Proceedings of the VLDB Endowment 15(6), 1243\u20131255 (2022)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"990_CR34","unstructured":"Karypis, G., Kumar, V.: Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices, (1997)"},{"key":"990_CR35","volume-title":"Attentive neural processes","author":"H Kim","year":"2019","unstructured":"Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, S.M.A., Rosenbaum, D., Vinyals, O., Teh, Y.W.: Attentive neural processes. In Proc, ICLR (2019)"},{"key":"990_CR36","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proc. ICLR. OpenReview.net, (2017)"},{"issue":"4","key":"990_CR37","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0060372","volume":"8","author":"J Lee","year":"2013","unstructured":"Lee, J., Lee, J.: Hidden information revealed by optimal community structure from a protein-complex bipartite network improves protein function prediction. PLoS ONE 8(4), e60372 (2013)","journal-title":"PLoS ONE"},{"key":"990_CR38","unstructured":"Leskovec, J., Mcauley, J.: Learning to discover social circles in ego networks. In: Advances in neural information processing systems, p. 25. (2012)"},{"issue":"5","key":"990_CR39","first-page":"6265","volume":"45","author":"J Li","year":"2022","unstructured":"Li, J., Huang, Y., Chang, H., Rong, Y.: Semi-supervised hierarchical graph classification. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 6265\u20136276 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"990_CR40","doi-asserted-by":"crossref","unstructured":"Li, L., Luo, S., Zhao, Y., Shan, C., Wang, Z., Qin, L.: Coclep: Contrastive learning-based semi-supervised community search. In: IEEE 39th ICDE, (2023)","DOI":"10.1109\/ICDE55515.2023.00191"},{"key":"990_CR41","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, X., Zhao, Y., Shan, W., Wang, Z., Yang, G., Wang, G.: Self-training gnn-based community search in large attributed heterogeneous information networks. In 2024 IEEE 40th International Conference on Data Engineering (ICDE), pages 2765\u20132778. IEEE, (2024)","DOI":"10.1109\/ICDE60146.2024.00216"},{"key":"990_CR42","unstructured":"Li, Z., Chen, Q., Koltun, V.: Combinatorial optimization with graph convolutional networks and guided tree search. In: Advances in neural information processing systems, p. 31. (2018)"},{"key":"990_CR43","doi-asserted-by":"crossref","unstructured":"Liu, F., Xue, S., Wu, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Yang, J., Yu, P.S.: Deep learning for community detection: progress, challenges and opportunities, (2020). arXiv:2005.08225 arXiv preprint","DOI":"10.24963\/ijcai.2020\/693"},{"key":"990_CR44","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhu, Y., Zhao, M., Huang, X., Xu, J., Gao, Y.: Vac: vertex-centric attributed community search. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 937\u2013948. IEEE, 2020","DOI":"10.1109\/ICDE48307.2020.00086"},{"key":"990_CR45","doi-asserted-by":"crossref","unstructured":"Liu, X., Pan, H., He, M., Song, Y., Jiang, X., Shang, L.: Neural subgraph isomorphism counting. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1959\u20131969, (2020)","DOI":"10.1145\/3394486.3403247"},{"key":"990_CR46","doi-asserted-by":"publisher","first-page":"2392","DOI":"10.1609\/aaai.v34i03.5619","volume":"34","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Li, C.-M., Jiang, H., He, K.: A learning based branch and bound for maximum common subgraph related problems. In Proceedings of the AAAI Conference on Artificial Intelligence 34, 2392\u20132399 (2020)","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"990_CR47","unstructured":"Lou, Z., You, J., Wen, C., Canedo, A., Leskovec, J.: Neural subgraph matching, (2020). arXiv:2007.03092 arXiv preprint"},{"key":"990_CR48","doi-asserted-by":"crossref","unstructured":"Luo, J., Cao, X., Xie, X., Qu, Q., Xu, Z., Jensen, C.S.: Efficient attribute-constrained co-located community search. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 1201\u20131212. IEEE, (2020)","DOI":"10.1109\/ICDE48307.2020.00108"},{"issue":"11","key":"990_CR49","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"990_CR50","unstructured":"Min, E., Chen, R., Bian, Y., Xu, T., Zhao, K., Huang, W., Zhao, P., Huang, J., Ananiadou, S., Rong, Y.: Transformer for graphs: An overview from architecture perspective. CoRR, abs\/2202.08455, (2022)"},{"key":"990_CR51","first-page":"1148","volume":"2022","author":"E Min","year":"2022","unstructured":"Min, E., Rong, Y., Bian, Y., Xu, T., Zhao, P., Huang, J., Ananiadou, S.: Divide-and-conquer: Post-user interaction network for fake news detection on social media. In Proceedings of the ACM web conference 2022, 1148\u20131158 (2022)","journal-title":"In Proceedings of the ACM web conference"},{"key":"990_CR52","doi-asserted-by":"crossref","unstructured":"Min, E., Rong, Y., Xu, T., Bian, Y., Luo, D., Lin, K., Huang, J., Ananiadou, S., Zhao, P.: Neighbour interaction based click-through rate prediction via graph-masked transformer. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 353\u2013362, (2022)","DOI":"10.1145\/3477495.3532031"},{"key":"990_CR53","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., De\u00a0Vries, H., Dumoulin, V., Courville, A.: Film: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI conference on artificial intelligence, volume\u00a032, (2018)","DOI":"10.1609\/aaai.v32i1.11671"},{"issue":"2","key":"990_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3433652","volume":"54","author":"P Ribeiro","year":"2021","unstructured":"Ribeiro, P., Paredes, P., Silva, M.E., Aparicio, D., Silva, F.: A survey on subgraph counting: concepts, algorithms, and applications to network motifs and graphlets. ACM Computing Surveys (CSUR) 54(2), 1\u201336 (2021)","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"990_CR55","unstructured":"Rong, Y., Huang, W., Xu, T., Huang, J.: Dropedge: Towards deep graph convolutional networks on node classification. In International Conference on Learning Representations, (2020)"},{"key":"990_CR56","doi-asserted-by":"crossref","unstructured":"Sarma, D., Alam, W., Saha, I., Alam, M.N., Alam, M.J., Hossain, S.: Bank fraud detection using community detection algorithm. In 2020 second international conference on inventive research in computing applications (ICIRCA), pages 642\u2013646. IEEE, (2020)","DOI":"10.1109\/ICIRCA48905.2020.9182954"},{"key":"990_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109035","volume":"250","author":"G Shen","year":"2022","unstructured":"Shen, G., Zhu, D., Chen, J., Kong, X.: Motif discovery based traffic pattern mining in attributed road networks. Knowl.-Based Syst. 250, 109035 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"990_CR58","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., Sun, Y.: Masked label prediction: Unified message passing model for semi-supervised classification. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, pages 1548\u20131554. ijcai.org, (2021)","DOI":"10.24963\/ijcai.2021\/214"},{"key":"990_CR59","doi-asserted-by":"crossref","unstructured":"Song, Y., Zhou, L., Yang, P., Wang, J., Wang, L.: Cs-dahin: Community search over dynamic attribute heterogeneous network. IEEE Transactions on Knowledge and Data Engineering, 2024","DOI":"10.1109\/TKDE.2024.3402258"},{"issue":"6825","key":"990_CR60","first-page":"268","volume":"410","author":"SH Strogatz","year":"2001","unstructured":"Strogatz, S.H.: Exploring complex networks. nature 410(6825), 268\u2013276 (2001)","journal-title":"Exploring complex networks. nature"},{"key":"990_CR61","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining, pages 565\u2013573, (2018)","DOI":"10.1145\/3159652.3159656"},{"key":"990_CR62","doi-asserted-by":"crossref","unstructured":"Tatonetti, N.P., Patrick, P.Y., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Science translational medicine, 4(125):125ra31\u2013125ra31, (2012)","DOI":"10.1126\/scitranslmed.3003377"},{"key":"990_CR63","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In Proc. NIPS, pages 5998\u20136008, (2017)"},{"issue":"20","key":"990_CR64","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. stat 1050(20), 10\u201348550 (2017)","journal-title":"Graph attention networks. stat"},{"key":"990_CR65","doi-asserted-by":"crossref","unstructured":"Wang, B., Li, J., Liu, Y., Cheng, J., Rong, Y., Wang, W., Tsung, F.: Deep insights into noisy pseudo labeling on graph data. Advances in Neural Information Processing Systems, 36, (2024)","DOI":"10.52202\/075280-3331"},{"key":"990_CR66","doi-asserted-by":"crossref","unstructured":"Wang, H., Hu, R., Zhang, Y., Qin, L., Wang, W., Zhang, W.: Neural subgraph counting with wasserstein estimator. In Proceedings of the 2022 International Conference on Management of Data, pages 160\u2013175, (2022)","DOI":"10.1145\/3514221.3526163"},{"issue":"9","key":"990_CR67","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.14778\/3665844.3665853","volume":"17","author":"J Wang","year":"2024","unstructured":"Wang, J., Wang, K., Lin, X., Zhang, W., Zhang, Y.: Efficient unsupervised community search with pre-trained graph transformer. Proc. VLDB Endow. 17(9), 2227\u20132240 (2024)","journal-title":"Proc. VLDB Endow."},{"issue":"4","key":"990_CR68","first-page":"1","volume":"1","author":"J Wang","year":"2024","unstructured":"Wang, J., Wang, K., Lin, X., Zhang, W., Zhang, Y.: Neural attributed community search at billion scale. Proceedings of the ACM on Management of Data 1(4), 1\u201325 (2024)","journal-title":"Proceedings of the ACM on Management of Data"},{"issue":"1","key":"990_CR69","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1162\/qss_a_00021","volume":"1","author":"K Wang","year":"2020","unstructured":"Wang, K., Shen, Z., Huang, C., Wu, C.-H., Dong, Y., Kanakia, A.: Microsoft academic graph: When experts are not enough. Quantitative Science Studies 1(1), 396\u2013413 (2020)","journal-title":"Quantitative Science Studies"},{"key":"990_CR70","doi-asserted-by":"crossref","unstructured":"Wang, Y., Gou, X., Xu, X., Geng, Y., Ke, X., Wu, T., Yu, Z., Chen, R., Wu, X.: Scalable community search over large-scale graphs based on graph transformer. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1680\u20131690, (2024)","DOI":"10.1145\/3626772.3657771"},{"key":"990_CR71","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ye, S., Xu, X., Geng, Y., Zhao, Z., Ke, X., Wu, T.: Scalable community search with accuracy guarantee on attributed graphs. In 40th IEEE International Conference on Data Engineering, ICDE 2024, Utrecht, The Netherlands, May 13-16, 2024, pages 2737\u20132750. IEEE, (2024)","DOI":"10.1109\/ICDE60146.2024.00214"},{"key":"990_CR72","doi-asserted-by":"crossref","unstructured":"Watts, D.J., Strogatz, S.H.: Collective dynamics of \u2018small-world\u2019networks. nature 393(6684), 440\u2013442 (1998)","DOI":"10.1038\/30918"},{"key":"990_CR73","doi-asserted-by":"crossref","unstructured":"Weinberger, K.Q., Dasgupta, A., Langford, J., Smola, A.J., Attenberg, J.: Feature hashing for large scale multitask learning. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009, volume 382 of ACM International Conference Proceeding Series, pages 1113\u20131120. ACM, (2009)","DOI":"10.1145\/1553374.1553516"},{"key":"990_CR74","volume-title":"A comprehensive survey on community detection with deep learning","author":"S Xing","year":"2022","unstructured":"Xing, S., Shan, X., Fanzhen, L., Jia, W., Jian, Y., Chuan, Z., Wenbin, H., Cecile, P., Surya, N., Di, J., et al.: A comprehensive survey on community detection with deep learning. IEEE Trans. Neural Netw. Learn, Syst (2022)"},{"key":"990_CR75","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: 7th International Conference on Learning Representations, ICLR 2019, OpenReview.net, (2019)"},{"key":"990_CR76","doi-asserted-by":"crossref","unstructured":"Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In Proceedings of the ACM SIGKDD workshop on mining data semantics, pages 1\u20138, (2012)","DOI":"10.1145\/2350190.2350193"},{"key":"990_CR77","unstructured":"Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning, pages 40\u201348. PMLR, (2016)"},{"key":"990_CR78","doi-asserted-by":"crossref","unstructured":"Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 974\u2013983, (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"990_CR79","doi-asserted-by":"crossref","unstructured":"Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple convolutional generative network for next item recommendation. In Proceedings of the twelfth ACM international conference on web search and data mining, pages 582\u2013590, (2019)","DOI":"10.1145\/3289600.3290975"},{"key":"990_CR80","unstructured":"Zeng, H., Zhou, H., Srivastava, A., Kannan, R., Prasanna, V.K.: Graphsaint: Graph sampling based inductive learning method. In: Proc. ICLR. OpenReview.net, (2020)"},{"key":"990_CR81","first-page":"4278","volume":"19","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Dong, Y., Wang, Y., Tang, J., Ding, M.: Prone: Fast and scalable network representation learning. In IJCAI 19, 4278\u20134284 (2019)","journal-title":"In IJCAI"},{"key":"990_CR82","unstructured":"Zhang, M., Chen, Y.: Inductive matrix completion based on graph neural networks. In: Proc. ICLR. OpenReview.net, (2020)"},{"key":"990_CR83","doi-asserted-by":"crossref","unstructured":"Zhang, T., Xiong, Y., Zhang, J., Zhang, Y., Jiao, Y., Zhu, Y.: Commdgi: community detection oriented deep graph infomax. In Proceedings of the 29th ACM international conference on information & knowledge management, pages 1843\u20131852, (2020)","DOI":"10.1145\/3340531.3412042"},{"key":"990_CR84","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yu, X., Cui, Z., Wu, S., Wen, Z., Wang, L.: Every document owns its structure: Inductive text classification via graph neural networks. In Proc. ACL, pages 334\u2013339. Association for Computational Linguistics, (2020)","DOI":"10.18653\/v1\/2020.acl-main.31"},{"key":"990_CR85","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Huang, X., Xu, J., Choi, B., Shang, Z.: Keyword-centric community search. In 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8-11, 2019, pages 422\u2013433. IEEE, (2019)","DOI":"10.1109\/ICDE.2019.00045"},{"key":"990_CR86","doi-asserted-by":"crossref","unstructured":"Zhao, K., Yu, J.X., Li, Q., Zhang, H., Rong, Y.: Learned sketch for subgraph counting: a holistic approach. The VLDB Journal, pages 1\u201326, (2023)","DOI":"10.1007\/s00778-023-00781-5"},{"key":"990_CR87","doi-asserted-by":"crossref","unstructured":"Zhao, K., Yu, J.X., Zhang, H., Li, Q., Rong, Y.: A learned sketch for subgraph counting. In Proceedings of the 2021 International Conference on Management of Data, pages 2142\u20132155, (2021)","DOI":"10.1145\/3448016.3457289"},{"key":"990_CR88","doi-asserted-by":"crossref","unstructured":"Zhao, K., Zhang, Z., Rong, Y., Yu, J.X., Huang, J.: Finding critical users in social communities via graph convolutions. IEEE Trans. Knowl. Data Eng. , (2021)","DOI":"10.1109\/TKDE.2021.3089763"},{"issue":"1","key":"990_CR89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms14049","volume":"8","author":"GX Zheng","year":"2017","unstructured":"Zheng, G.X., Terry, J.M., Belgrader, P., Ryvkin, P., Bent, Z.W., Wilson, R., Ziraldo, S.B., Wheeler, T.D., McDermott, G.P., Zhu, J., et al.: Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8(1), 1\u201312 (2017)","journal-title":"Nat. Commun."},{"key":"990_CR90","doi-asserted-by":"crossref","unstructured":"Zhu, Y., He, J., Ye, J., Qin, L., Huang, X., Yu, J.X.: When structure meets keywords: Cohesive attributed community search. In M.\u00a0d\u2019Aquin, S.\u00a0Dietze, C.\u00a0Hauff, E.\u00a0Curry, and P.\u00a0Cudr\u00e9-Mauroux, editors, CIKM \u201920: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, pages 1913\u20131922. ACM, (2020)","DOI":"10.1145\/3340531.3412006"}],"container-title":["The VLDB Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-026-00990-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00778-026-00990-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-026-00990-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:43:09Z","timestamp":1783665789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00778-026-00990-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":90,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["990"],"URL":"https:\/\/doi.org\/10.1007\/s00778-026-00990-8","relation":{},"ISSN":["1066-8888","0949-877X"],"issn-type":[{"value":"1066-8888","type":"print"},{"value":"0949-877X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"31 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"36"}}