{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T04:13:54Z","timestamp":1772424834814,"version":"3.50.1"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T00:00:00Z","timestamp":1684713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2021YFB1714800"],"award-info":[{"award-number":["2021YFB1714800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62202164"],"award-info":[{"award-number":["62202164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"S&T Program of Hebei","award":["21340301D"],"award-info":[{"award-number":["21340301D"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2022MS018"],"award-info":[{"award-number":["2022MS018"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            Social network alignment, which aims to uncover the correspondence across different social networks, shows fundamental importance in a wide spectrum of applications such as cross-domain recommendation and information propagation. In the literature, the vast majority of the existing studies focus on the social network alignment at user level. In practice, the user-level alignment usually relies on abundant personal information and high-quality supervision, which is expensive and even impossible in the real-world scenario. Alternatively, we propose to study the problem of social group alignment across different social networks, focusing on the interests of social groups rather than personal information. However, social group alignment is non-trivial and faces significant challenges in both (i) feature inconsistency across different social networks and (ii) group discovery within a social network. To bridge this gap, we present a novel\n            <jats:sc>GroupAligner<\/jats:sc>\n            , a deep reinforcement learning with domain adaptation for social group alignment. In\n            <jats:sc>GroupAligner<\/jats:sc>\n            , to address the first issue, we propose the cycle domain adaptation approach with the Wasserstein distance to transfer the knowledge from the source social network, aligning the feature space of social networks in the distribution level. To address the second issue, we model the group discovery as a sequential decision process with reinforcement learning in which the policy is parameterized by a proposed\n            <jats:bold>\n              <jats:underline>p<\/jats:underline>\n              roximity-enhanced\n              <jats:underline>G<\/jats:underline>\n              raph\n              <jats:underline>N<\/jats:underline>\n              eural\n              <jats:underline>N<\/jats:underline>\n              etwork (pGNN)\n            <\/jats:bold>\n            and a GNN-based discriminator to score the reward. Finally, we utilize pre-training and teacher forcing to stabilize the learning process of\n            <jats:sc>GroupAligner<\/jats:sc>\n            . Extensive experiments on several real-world datasets are conducted to evaluate\n            <jats:sc>GroupAligner<\/jats:sc>\n            , and experimental results show that\n            <jats:sc>GroupAligner<\/jats:sc>\n            outperforms the alternative methods for social group alignment.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3580509","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T12:04:53Z","timestamp":1674561893000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["<scp>GroupAligner<\/scp>\n            : A Deep Reinforcement Learning with Domain Adaptation for Social Group Alignment"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4562-2279","authenticated-orcid":false,"given":"Li","family":"Sun","sequence":"first","affiliation":[{"name":"North China Electric Power University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0513-0675","authenticated-orcid":false,"given":"Yang","family":"Du","sequence":"additional","affiliation":[{"name":"The University of Edinburgh, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3424-206X","authenticated-orcid":false,"given":"Shuai","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2900-4908","authenticated-orcid":false,"given":"Junda","family":"Ye","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5074-1606","authenticated-orcid":false,"given":"Feiyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1234-0515","authenticated-orcid":false,"given":"Fuxin","family":"Ren","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8433-6860","authenticated-orcid":false,"given":"Mingchen","family":"Liang","sequence":"additional","affiliation":[{"name":"North China Electric Power University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4608-2852","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2888-1922","authenticated-orcid":false,"given":"Shuhai","family":"Wang","sequence":"additional","affiliation":[{"name":"Shijiazhuang Tiedao University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137640"},{"key":"e_1_3_2_3_2","volume-title":"K-means++: The Advantages of Careful Seeding","author":"Arthur David","year":"2006","unstructured":"David Arthur and Sergei Vassilvitskii. 2006. K-means++: The Advantages of Careful Seeding. Technical Report. Stanford University."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.05.008"},{"key":"e_1_3_2_5_2","first-page":"21","volume-title":"Proceedings of ICDM","author":"Bian Yuchen","year":"2017","unstructured":"Yuchen Bian, Jingchao Ni, Wei Cheng, and Xiang Zhang. 2017. Many heads are better than one: Local community detection by the multi-walker chain. In Proceedings of ICDM. IEEE, 21\u201330."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_3_2_7_2","first-page":"817","volume-title":"Proceedings of VLDB","author":"Cai Hongyun","year":"2017","unstructured":"Hongyun Cai, Vincent W. Zheng, Fanwei Zhu, Kevin Chen-Chuan Chang, and Zi Huang. 2017. From community detection to community profiling. In Proceedings of VLDB. 817\u2013828."},{"key":"e_1_3_2_8_2","first-page":"2598","volume-title":"Proceedings of IJCAI","author":"Cai Xiao","year":"2013","unstructured":"Xiao Cai, Feiping Nie, and Heng Huang. 2013. Multi-view k-means clustering on big data. In Proceedings of IJCAI. 2598\u20132604."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132925"},{"key":"e_1_3_2_10_2","first-page":"459","volume-title":"Proceedings of SIGMOD","author":"Chang Lijun","year":"2015","unstructured":"Lijun Chang, Xuemin Lin, Lu Qin, Jeffrey Xu Yu, and Wenjie Zhang. 2015. Index-based optimal algorithms for computing Steiner components with maximum connectivity. In Proceedings of SIGMOD. ACM, 459\u2013474."},{"key":"e_1_3_2_11_2","first-page":"205","volume-title":"Proceedings of SIGMOD","author":"Chang Lijun","year":"2013","unstructured":"Lijun Chang, Jeffrey Xu Yu, Lu Qin, Xuemin Lin, Chengfei Liu, and Weifa Liang. 2013. Efficiently computing k-edge connected components via graph decomposition. In Proceedings of SIGMOD. ACM, 205\u2013216."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231755"},{"key":"e_1_3_2_13_2","first-page":"587","volume-title":"Proceedings of CIKM","author":"Chen Zheng","year":"2017","unstructured":"Zheng Chen, Xinli Yu, Bo Song, Jianliang Gao, Xiaohua Hu, and Wei-Shih Yang. 2017. Community-based network alignment for large attributed network. In Proceedings of CIKM. ACM, 587\u2013596."},{"key":"e_1_3_2_14_2","first-page":"8789","volume-title":"Proceedings of CVPR","author":"Choi Yunjey","year":"2018","unstructured":"Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2018. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of CVPR. IEEE, 8789\u20138797."},{"key":"e_1_3_2_15_2","article-title":"Deep adversarial network alignment","volume":"1902","author":"Derr Tyler","year":"2019","unstructured":"Tyler Derr, Hamid Karimi, Xiaorui Liu, Jiejun Xu, and Jiliang Tang. 2019. Deep adversarial network alignment. CoRR abs\/1902.10307 (2019).","journal-title":"CoRR"},{"key":"e_1_3_2_16_2","first-page":"2251","volume-title":"Proceedings of IJCAI","author":"Du Xingbo","year":"2019","unstructured":"Xingbo Du, Junchi Yan, and Hongyuan Zha. 2019. Joint link prediction and network alignment via cross-graph embedding. In Proceedings of IJCAI. 2251\u20132257."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994538"},{"issue":"11","key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1109\/TKDE.2018.2872982","article-title":"Effective and efficient community search over large directed graphs","volume":"31","author":"Fang Yixiang","year":"2018","unstructured":"Yixiang Fang, Zhongran Wang, Reynold Cheng, Hongzhi Wang, and Jiafeng Hu. 2018. Effective and efficient community search over large directed graphs. IEEE Trans. Knowl. Data Eng. 31, 11 (2018), 2093\u20132107.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"e_1_3_2_20_2","first-page":"5767","volume-title":"Proceedings of Advances in NeurIPS","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani, Faruk Ahmed, Mart\u00edn Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017. Improved training of Wasserstein GANs. In Proceedings of Advances in NeurIPS. 5767\u20135777."},{"key":"e_1_3_2_21_2","first-page":"1024","volume-title":"Proceedings of Advances in NeurIPS","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of Advances in NeurIPS. 1024\u20131034."},{"key":"e_1_3_2_22_2","first-page":"820","volume-title":"Proceedings of Advances in NeurIPS","author":"He Di","year":"2016","unstructured":"Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. 2016. Dual learning for machine translation. In Proceedings of Advances in NeurIPS. 820\u2013828."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.14778\/3099622.3099626"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.14778\/2856318.2856323"},{"key":"e_1_3_2_25_2","first-page":"426","volume-title":"Proceedings of SIGKDD","author":"Ji Gao","year":"2021","unstructured":"Gao Ji, Huang Xiao, and Li Jundong. 2021. Unsupervised graph alignment with Wasserstein distance discriminator. In Proceedings of SIGKDD. ACM, 426\u2013435."},{"key":"e_1_3_2_26_2","first-page":"784","volume-title":"Proceedings of ACM Web Conference (WWW)","author":"Jia Yuting","year":"2019","unstructured":"Yuting Jia, Qinqin Zhang, Weinan Zhang, and Xinbing Wang. 2019. CommunityGAN: Community detection with generative adversarial nets. In Proceedings of ACM Web Conference (WWW). ACM, 784\u2013794."},{"key":"e_1_3_2_27_2","article-title":"QD-GCN: Query-driven graph convolutional networks for attributed community search","volume":"2104","author":"Jiang Yuli","year":"2021","unstructured":"Yuli Jiang, Yu Rong, Hong Cheng, Xin Huang, Kangfei Zhao, and Junzhou Huang. 2021. QD-GCN: Query-driven graph convolutional networks for attributed community search. CoRR abs\/2104.03583 (2021).","journal-title":"CoRR"},{"key":"e_1_3_2_28_2","first-page":"281","volume-title":"Proceedings of IEEE BigData","author":"Jin Songchang","year":"2014","unstructured":"Songchang Jin, Jiawei Zhang, Philip S. Yu, Shuqiang Yang, and Aiping Li. 2014. Synergistic partitioning in multiple large scale social networks. In Proceedings of IEEE BigData. IEEE, 281\u2013290."},{"key":"e_1_3_2_29_2","first-page":"996","volume-title":"Proceedings of AAAI","author":"Li Chaozhuo","year":"2019","unstructured":"Chaozhuo Li, Senzhang Wang, Yukun Wang, Philip S. Yu, Yanbo Liang, Yun Liu, and Zhoujun Li. 2019. Adversarial learning for weakly-supervised social network alignment. In Proceedings of AAAI. AAAI Press, 996\u20131003."},{"key":"e_1_3_2_30_2","first-page":"447","volume-title":"Proceedings of CIKM","author":"Li Chaozhuo","year":"2018","unstructured":"Chaozhuo Li, Senzhang Wang, Philip S. Yu, Lei Zheng, Xiaoming Zhang, Zhoujun Li, and Yanbo Liang. 2018. Distribution distance minimization for unsupervised user identity linkage. In Proceedings of CIKM. ACM, 447\u2013456."},{"key":"e_1_3_2_31_2","first-page":"2157","volume-title":"Proceedings of EMNLP","author":"Li Jiwei","year":"2017","unstructured":"Jiwei Li, Will Monroe, Tianlin Shi, S\u00e9bastien Jean, Alan Ritter, and Dan Jurafsky. 2017. Adversarial learning for neural dialogue generation. In Proceedings of EMNLP. Association for Computational Linguistics, 2157\u20132169."},{"key":"e_1_3_2_32_2","first-page":"25","volume-title":"Proceedings of ICDE","author":"Lim Sungsu","year":"2016","unstructured":"Sungsu Lim, Junghoon Kim, and Jae-Gil Lee. 2016. BlackHole: Robust community detection inspired by graph drawing. In Proceedings of ICDE. IEEE, 25\u201336."},{"key":"e_1_3_2_33_2","first-page":"2739","volume-title":"Proceedings of IJCAI","author":"Lin Xuan","year":"2020","unstructured":"Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, and Xiangxiang Zeng. 2020. KGNN: Knowledge graph neural network for drug-drug interaction prediction. In Proceedings of IJCAI. 2739\u20132745."},{"key":"e_1_3_2_34_2","first-page":"495","volume-title":"Proceedings of WSDM","author":"Liu Jing","year":"2013","unstructured":"Jing Liu, Fan Zhang, Xinying Song, Young-In Song, Chin-Yew Lin, and Hsiao-Wuen Hon. 2013. What\u2019s in a name?: An unsupervised approach to link users across communities. In Proceedings of WSDM. ACM, 495\u2013504."},{"key":"e_1_3_2_35_2","first-page":"1774","volume-title":"Proceedings of IJCAI","author":"Liu Li","year":"2016","unstructured":"Li Liu, William K. Cheung, Xin Li, and Lejian Liao. 2016. Aligning users across social networks using network embedding. In Proceedings of IJCAI. 1774\u20131780."},{"key":"e_1_3_2_36_2","first-page":"2183","volume-title":"Proceedings of SIGMOD","author":"Liu Qing","year":"2020","unstructured":"Qing Liu, Minjun Zhao, Xin Huang, Jianliang Xu, and Yunjun Gao. 2020. Truss-based community search over large directed graphs. In Proceedings of SIGMOD. ACM, 2183\u20132197."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3100864"},{"key":"e_1_3_2_38_2","first-page":"51","volume-title":"Proceedings of SIGMOD","author":"Liu Siyuan","year":"2014","unstructured":"Siyuan Liu, Shuhui Wang, Feida Zhu, Jinbo Zhang, and Ramayya Krishnan. 2014. Hydra: Large-scale social identity linkage via heterogeneous behavior modeling. In Proceedings of SIGMOD. ACM, 51\u201362."},{"key":"e_1_3_2_39_2","first-page":"1823","volume-title":"Proceedings of IJCAI","author":"Man Tong","year":"2016","unstructured":"Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin, and Xueqi Cheng. 2016. Predict anchor links across social networks via an embedding approach. In Proceedings of IJCAI. 1823\u20131829."},{"key":"e_1_3_2_40_2","first-page":"1775","volume-title":"Proceedings of SIGKDD","author":"Mu Xin","year":"2016","unstructured":"Xin Mu, Feida Zhu, Ee-Peng Lim, Jing Xiao, Jianzong Wang, and Zhi-Hua Zhou. 2016. User identity linkage by latent user space modelling. In Proceedings of SIGKDD. ACM, 1775\u20131784."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0601602103"},{"key":"e_1_3_2_42_2","first-page":"1104","volume-title":"Proceedings of ACM Web Conference (WWW)","author":"Pei Shichao","year":"2022","unstructured":"Shichao Pei, Lu Yu, Guoxian Yu, and Xiangliang Zhang. 2022. Graph alignment with noisy supervision. In Proceedings of ACM Web Conference (WWW). ACM, 1104\u20131114."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.01.043"},{"issue":"4","key":"e_1_3_2_44_2","first-page":"69:1","article-title":"Reinforced neighborhood selection guided multi-relational graph neural networks","volume":"40","author":"Peng Hao","year":"2022","unstructured":"Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, and Philip S. Yu. 2022. Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans. Inf. Syst. 40, 4 (2022), 69:1\u201369:46.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3144993"},{"key":"e_1_3_2_46_2","first-page":"1255","volume-title":"Proceedings of CIKM","author":"Qin Kyle Kai","year":"2020","unstructured":"Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, and Danai Koutra. 2020. G-CREWE: Graph CompREssion with embedding for network alignment. In Proceedings of CIKM. ACM, 1255\u20131264."},{"key":"e_1_3_2_47_2","first-page":"2527","volume-title":"Proceedings of AAAI","author":"Shen Xiaobo","year":"2017","unstructured":"Xiaobo Shen, Weiwei Liu, Ivor Tsang, Fumin Shen, and Quan-Sen Sun. 2017. Compressed k-means for large-scale clustering. In Proceedings of AAAI. 2527\u20132533."},{"key":"e_1_3_2_48_2","volume-title":"Proceedings of AAAI","author":"Shiokawa Hiroaki","year":"2013","unstructured":"Hiroaki Shiokawa, Yasuhiro Fujiwara, and Makoto Onizuka. 2013. Fast algorithm for modularity-based graph clustering. In Proceedings of AAAI."},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3068777.3068781"},{"key":"e_1_3_2_50_2","first-page":"939","volume-title":"Proceedings of SIGKDD","author":"Sozio Mauro","year":"2010","unstructured":"Mauro Sozio and Aristides Gionis. 2010. The community-search problem and how to plan a successful cocktail party. In Proceedings of SIGKDD. ACM, 939\u2013948."},{"key":"e_1_3_2_51_2","first-page":"1827","volume-title":"Proceedings of CIKM","author":"Sun Li","year":"2022","unstructured":"Li Sun, Junda Ye, Hao Peng, and Philip S. Yu. 2022. A self-supervised riemannian GNN with time varying curvature for temporal graph learning. In Proceedings of CIKM. ACM, 1827\u20131836."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3152502"},{"key":"e_1_3_2_53_2","first-page":"501","volume-title":"Proceedings of ICDM","author":"Sun Li","year":"2020","unstructured":"Li Sun, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Yang Du, Sen Su, and Philip S. Yu. 2020. Perfect: A hyperbolic embedding for joint user and community alignment. In Proceedings of ICDM. IEEE, 501\u2013510."},{"key":"e_1_3_2_54_2","first-page":"511","volume-title":"Proceedings of ICDM","author":"Sun Qingyun","year":"2020","unstructured":"Qingyun Sun, Hao Peng, Jianxin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, and Lifang He. 2020. Pairwise learning for name disambiguation in large-scale heterogeneous academic networks. In Proceedings of ICDM. IEEE, 511\u2013520."},{"key":"e_1_3_2_55_2","first-page":"638","volume-title":"Proceedings of IJCAI","author":"Szczepa\u0144ski Piotr Lech","year":"2015","unstructured":"Piotr Lech Szczepa\u0144ski, Aleksy Stanis\u0142aw Barcz, Tomasz Pawe\u0142 Michalak, and Talal Rahwan. 2015. The game-theoretic interaction index on social networks with applications to link prediction and community detection. In Proceedings of IJCAI. 638\u2013644."},{"key":"e_1_3_2_56_2","first-page":"1067","volume-title":"Proceedings of ACM Web Conference (WWW)","author":"Tang Jian","year":"2015","unstructured":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale information network embedding. In Proceedings of ACM Web Conference (WWW). ACM, 1067\u20131077."},{"key":"e_1_3_2_57_2","volume-title":"Proceedings of ICLR","author":"Vinyals Oriol","year":"2016","unstructured":"Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. 2016. Order matters: Sequence to sequence for sets. In Proceedings of ICLR. Retrieved from http:\/\/arxiv.org\/abs\/1511.06391."},{"key":"e_1_3_2_58_2","first-page":"3670","volume-title":"Proceedings of IJCAI","author":"Wang Chun","year":"2019","unstructured":"Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Attributed graph clustering: A deep attentional embedding approach. In Proceedings of IJCAI. 3670\u20133676."},{"key":"e_1_3_2_59_2","first-page":"889","volume-title":"Proceedings of CIKM","author":"Wang Chun","year":"2017","unstructured":"Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. 2017. MGAE: Marginalized graph autoencoder for graph clustering. In Proceedings of CIKM. ACM, 889\u2013898."},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.14778\/2752939.2752948"},{"key":"e_1_3_2_61_2","first-page":"1913","volume-title":"Proceedings of SIGKDD","author":"Xiong Hao","year":"2021","unstructured":"Hao Xiong, Junchi Yan, and Li Pan. 2021. Contrastive multi-view multiplex network embedding with applications to robust network alignment. In Proceedings of SIGKDD. ACM, 1913\u20131923."},{"key":"e_1_3_2_62_2","first-page":"3907","volume-title":"Proceedings of ACM Web Conference (WWW)","author":"Yan Yuchen","year":"2021","unstructured":"Yuchen Yan, Si Zhang, and Hanghang Tong. 2021. BRIGHT: A bridging algorithm for network alignment. In Proceedings of ACM Web Conference (WWW). ACM, 3907\u20133917."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/2433396.2433471"},{"key":"e_1_3_2_64_2","first-page":"323","volume-title":"Proceedings of WSDM","author":"Yang Jaewon","year":"2014","unstructured":"Jaewon Yang, Julian McAuley, and Jure Leskovec. 2014. Detecting cohesive and 2-mode communities indirected and undirected networks. In Proceedings of WSDM. ACM, 323\u2013332."},{"key":"e_1_3_2_65_2","first-page":"2852","volume-title":"Proceedings of AAAI","author":"Yu Lantao","year":"2017","unstructured":"Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. SeqGAN: Sequence generative adversarial nets with policy gradient. In Proceedings of AAAI. AAAI Press, 2852\u20132858."},{"issue":"5","key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/TKDE.2017.2783933","article-title":"Index-based densest clique percolation community search in networks","volume":"30","author":"Yuan Long","year":"2017","unstructured":"Long Yuan, Lu Qin, Wenjie Zhang, Lijun Chang, and Jianye Yang. 2017. Index-based densest clique percolation community search in networks. IEEE Trans. Knowl. Data Eng. 30, 5 (2017), 922\u2013935.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_2_67_2","first-page":"41","volume-title":"Proceedings of KDD","author":"Zafarani Reza","year":"2013","unstructured":"Reza Zafarani and Huan Liu. 2013. Connecting users across social media sites: A behavioral-modeling approach. In Proceedings of KDD. ACM, 41\u201349."},{"key":"e_1_3_2_68_2","first-page":"327","volume-title":"Proceedings of CIKM","author":"Zhang Jing","year":"2018","unstructured":"Jing Zhang, Bo Chen, Xianming Wang, Hong Chen, Cuiping Li, Fengmei Jin, Guojie Song, and Yutao Zhang. 2018. MEgo2Vec: Embedding matched ego networks for user alignment across social networks. In Proceedings of CIKM. ACM, 327\u2013336."},{"key":"e_1_3_2_69_2","first-page":"2125","volume-title":"Proceedings of IJCAI","author":"Zhang Jiawei","year":"2015","unstructured":"Jiawei Zhang and Philip S. Yu. 2015. Integrated anchor and social link predictions across social networks. In Proceedings of IJCAI. 2125\u20132132."},{"key":"e_1_3_2_70_2","first-page":"2212","volume-title":"Proceedings of SIGKDD","author":"Zhang Si","year":"2021","unstructured":"Si Zhang, Hanghang Tong, Long Jin, Yinglong Xia, and Yunsong Guo. 2021. Balancing consistency and disparity in network alignment. In Proceedings of SIGKDD. ACM, 2212\u20132222."},{"key":"e_1_3_2_71_2","first-page":"2344","volume-title":"Proceedings of ACM Web Conference (WWW)","author":"Zhang Si","year":"2019","unstructured":"Si Zhang, Hanghang Tong, Ross Maciejewski, and Tina Eliassi-Rad. 2019. Multilevel network alignment. In Proceedings of ACM Web Conference (WWW). ACM, 2344\u20132354."},{"key":"e_1_3_2_72_2","first-page":"1485","volume-title":"Proceedings of SIGKDD","author":"Zhang Yutao","year":"2015","unstructured":"Yutao Zhang, Jie Tang, Zhilin Yang, Jian Pei, and Philip S. Yu. 2015. COSNET: Connecting heterogeneous social networks with local and global consistency. In Proceedings of SIGKDD. ACM, 1485\u20131494."},{"key":"e_1_3_2_73_2","first-page":"1103","volume-title":"Proceedings of SIGKDD","author":"Zhang Yao","year":"2020","unstructured":"Yao Zhang, Yun Xiong, Yun Ye, Tengfei Liu, Weiqiang Wang, Yangyong Zhu, and Philip S. Yu. 2020. SEAL: Learning heuristics for community detection with generative adversarial networks. In Proceedings of SIGKDD. ACM, 1103\u20131113."},{"key":"e_1_3_2_74_2","first-page":"5714","volume-title":"Proceedings of AAAI","author":"Zhong Zexuan","year":"2018","unstructured":"Zexuan Zhong, Yong Cao, Mu Guo, and Zaiqing Nie. 2018. CoLink: An unsupervised framework for user identity linkage. In Proceedings of AAAI. 5714\u20135721."},{"key":"e_1_3_2_75_2","first-page":"686","volume-title":"Proceedings of INFOCOM","author":"Zhou Fan","year":"2020","unstructured":"Fan Zhou, Chengtai Cao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Ji Geng. 2020. Fast network alignment via graph meta-learning. In Proceedings of INFOCOM. 686\u2013695."},{"key":"e_1_3_2_76_2","first-page":"1313","volume-title":"Proceedings of INFOCOM","author":"Zhou Fan","year":"2018","unstructured":"Fan Zhou, Lei Liu, Kunpeng Zhang, Goce Trajcevski, Jin Wu, and Ting Zhong. 2018. DeepLink: A deep learning approach for user identity linkage. In Proceedings of INFOCOM. IEEE, 1313\u20131321."}],"container-title":["ACM Transactions on the Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580509","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580509","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:35Z","timestamp":1750178795000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580509"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":75,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,8,31]]}},"alternative-id":["10.1145\/3580509"],"URL":"https:\/\/doi.org\/10.1145\/3580509","relation":{},"ISSN":["1559-1131","1559-114X"],"issn-type":[{"value":"1559-1131","type":"print"},{"value":"1559-114X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,22]]},"assertion":[{"value":"2022-01-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-10-20","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}