{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:17:58Z","timestamp":1784179078275,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":73,"publisher":"ACM","funder":[{"name":"Hong Kong RGC R1015-23"},{"name":"The National Natural Science Foundation of China","award":["62302414"],"award-info":[{"award-number":["62302414"]}]},{"name":"The Hong Kong RGC ECS grant","award":["22202623"],"award-info":[{"award-number":["22202623"]}]},{"name":"The Guangdong Basic and Applied Basic Research Foundation","award":["2023B1515130002"],"award-info":[{"award-number":["2023B1515130002"]}]},{"name":"The Huawei Gift Fund"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,13]]},"DOI":"10.1145\/3726302.3730079","type":"proceedings-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T01:18:36Z","timestamp":1752455916000},"page":"771-781","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SAFT: Structure-aware Transformers for Textual Interaction Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1279-4357","authenticated-orcid":false,"given":"Hongtao","family":"Wang","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7284-3096","authenticated-orcid":false,"given":"Renchi","family":"Yang","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9757-4347","authenticated-orcid":false,"given":"Hewen","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4769-3716","authenticated-orcid":false,"given":"Haoran","family":"Zheng","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9404-5848","authenticated-orcid":false,"given":"Jianliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,13]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1038","article-title":"Edge classification in networks","author":"Aggarwal Charu","year":"2016","unstructured":"Charu Aggarwal, Gewen He, and Peixiang Zhao. 2016. Edge classification in networks. In ICDE. IEEE, 1038-1049.","journal-title":"ICDE. IEEE"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102597"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.01.048"},{"key":"e_1_3_2_1_4_1","first-page":"81","article-title":"Relational topic models for document networks. In Artificial intelligence and statistics","author":"Chang Jonathan","year":"2009","unstructured":"Jonathan Chang and David Blei. 2009. Relational topic models for document networks. In Artificial intelligence and statistics. PMLR, 81-88.","journal-title":"PMLR"},{"key":"e_1_3_2_1_5_1","first-page":"1725","article-title":"Simple and deep graph convolutional networks","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML. PMLR, 1725-1735.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3655103.3655110"},{"key":"e_1_3_2_1_7_1","volume-title":"Graphs: New Directions in Topological Imbalance. arXiv preprint arXiv:2406.11685","author":"Cheng Xueqi","year":"2024","unstructured":"Xueqi Cheng, Yu Wang, Yuying Zhao, Charu C Aggarwal, Tyler Derr, et al., 2024. Edge Classification on Graphs: New Directions in Topological Imbalance. arXiv preprint arXiv:2406.11685 (2024)."},{"key":"e_1_3_2_1_8_1","volume-title":"Node feature extraction by self-supervised multi-scale neighborhood prediction. arXiv preprint arXiv:2111.00064","author":"Chien Eli","year":"2021","unstructured":"Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, and Inderjit S Dhillon. 2021. Node feature extraction by self-supervised multi-scale neighborhood prediction. arXiv preprint arXiv:2111.00064 (2021)."},{"key":"e_1_3_2_1_9_1","volume-title":"NeurIPS","volume":"29","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. NeurIPS, Vol. 29 (2016)."},{"key":"e_1_3_2_1_10_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_11_1","first-page":"3651","article-title":"On the equivalence of decoupled graph convolution network and label propagation","author":"Dong Hande","year":"2021","unstructured":"Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, and Peng Cui. 2021. On the equivalence of decoupled graph convolution network and label propagation. In TheWebConf. 3651-3662.","journal-title":"TheWebConf."},{"key":"e_1_3_2_1_12_1","first-page":"1","article-title":"Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs","author":"Fathony Rizal","year":"2023","unstructured":"Rizal Fathony, Jenn Ng, and Jia Chen. 2023. Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs. In IJCNN. IEEE, 1-10.","journal-title":"IJCNN. IEEE"},{"key":"e_1_3_2_1_13_1","volume-title":"Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997","author":"Gasteiger Johannes","year":"2018","unstructured":"Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 (2018)."},{"key":"e_1_3_2_1_14_1","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In ICML. PMLR, 1263-1272.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_15_1","first-page":"9211","article-title":"Exploiting edge features for graph neural networks","author":"Gong Liyu","year":"2019","unstructured":"Liyu Gong and Qiang Cheng. 2019. Exploiting edge features for graph neural networks. In CVPR. 9211-9219.","journal-title":"CVPR."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_17_1","volume-title":"NeurIPS","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. NeurIPS, Vol. 30 (2017)."},{"key":"e_1_3_2_1_18_1","volume-title":"Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654","author":"He Pengcheng","year":"2020","unstructured":"Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654 (2020)."},{"key":"e_1_3_2_1_19_1","first-page":"507","article-title":"Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering","author":"He Ruining","year":"2016","unstructured":"Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In TheWebConf. 507-517.","journal-title":"TheWebConf."},{"key":"e_1_3_2_1_20_1","first-page":"9099","article-title":"Transformer quality in linear time","author":"Hua Weizhe","year":"2022","unstructured":"Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc Le. 2022. Transformer quality in linear time. In ICML. PMLR, 9099-9117.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775126"},{"key":"e_1_3_2_1_22_1","first-page":"2656","article-title":"CensNet: Convolution with Edge-Node Switching in Graph Neural Networks","author":"Jiang Xiaodong","year":"2019","unstructured":"Xiaodong Jiang, Pengsheng Ji, and Sheng Li. 2019. CensNet: Convolution with Edge-Node Switching in Graph Neural Networks.. In IJCAI. 2656-2662.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_23_1","volume-title":"Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks. In ICLR.","author":"Jin Bowen","year":"2022","unstructured":"Bowen Jin, Yu Zhang, Yu Meng, and Jiawei Han. 2022. Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks. In ICLR."},{"key":"e_1_3_2_1_24_1","volume-title":"Edgeformers: Graph-empowered transformers for representation learning on textual-edge networks. arXiv preprint arXiv:2302.11050","author":"Jin Bowen","year":"2023","unstructured":"Bowen Jin, Yu Zhang, Yu Meng, and Jiawei Han. 2023. Edgeformers: Graph-empowered transformers for representation learning on textual-edge networks. arXiv preprint arXiv:2302.11050 (2023)."},{"key":"e_1_3_2_1_25_1","first-page":"7534","article-title":"Edge representation learning with hypergraphs","volume":"34","author":"Jo Jaehyeong","year":"2021","unstructured":"Jaehyeong Jo, Jinheon Baek, Seul Lee, Dongki Kim, Minki Kang, and Sung Ju Hwang. 2021. Edge representation learning with hypergraphs. NeurIPS, Vol. 34 (2021), 7534-7546.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289026"},{"key":"e_1_3_2_1_27_1","first-page":"11","article-title":"Edge-labeling graph neural network for few-shot learning","author":"Kim Jongmin","year":"2019","unstructured":"Jongmin Kim, Taesup Kim, Sungwoong Kim, and Chang D Yoo. 2019. Edge-labeling graph neural network for few-shot learning. In CVPR. 11-20.","journal-title":"CVPR."},{"key":"e_1_3_2_1_28_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01164627"},{"key":"e_1_3_2_1_30_1","first-page":"9119","article-title":"On the Sentence Embeddings from Pre-trained Language Models","author":"Li Bohan","year":"2020","unstructured":"Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020. On the Sentence Embeddings from Pre-trained Language Models. In EMNLP. 9119-9130.","journal-title":"EMNLP."},{"key":"e_1_3_2_1_31_1","first-page":"223","article-title":"Adsgnn: Behavior-graph augmented relevance modeling in sponsored search","author":"Li Chaozhuo","year":"2021","unstructured":"Chaozhuo Li, Bochen Pang, Yuming Liu, Hao Sun, Zheng Liu, Xing Xie, Tianqi Yang, Yanling Cui, Liangjie Zhang, and Qi Zhang. 2021. Adsgnn: Behavior-graph augmented relevance modeling in sponsored search. In SIGIR. 223-232.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_32_1","volume-title":"Uctopic: Unsupervised contrastive learning for phrase representations and topic mining. arXiv preprint arXiv:2202.13469","author":"Li Jiacheng","year":"2022","unstructured":"Jiacheng Li, Jingbo Shang, and Julian McAuley. 2022. Uctopic: Unsupervised contrastive learning for phrase representations and topic mining. arXiv preprint arXiv:2202.13469 (2022)."},{"key":"e_1_3_2_1_33_1","first-page":"338","article-title":"Towards deeper graph neural networks","author":"Liu Meng","year":"2020","unstructured":"Meng Liu, Hongyang Gao, and Shuiwang Ji. 2020. Towards deeper graph neural networks. In SIGKDD. 338-348.","journal-title":"SIGKDD."},{"key":"e_1_3_2_1_34_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_35_1","first-page":"1","article-title":"Random walks on graphs","volume":"2","author":"Lov\u00e1sz L\u00e1szl\u00f3","year":"1993","unstructured":"L\u00e1szl\u00f3 Lov\u00e1sz. 1993. Random walks on graphs. Combinatorics, Vol. 2, 1--46 (1993), 4.","journal-title":"Combinatorics"},{"key":"e_1_3_2_1_36_1","volume-title":"Mohamed Abdur Rahman, and Saleh Basalamah.","author":"Madkour Amgad","year":"2017","unstructured":"Amgad Madkour, Walid G Aref, Faizan Ur Rehman, Mohamed Abdur Rahman, and Saleh Basalamah. 2017. A survey of shortest-path algorithms. arXiv preprint arXiv:1705.02044 (2017)."},{"key":"e_1_3_2_1_37_1","first-page":"2327","article-title":"Item tagging for information retrieval: A tripartite graph neural network based approach","author":"Mao Kelong","year":"2020","unstructured":"Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, and Xiuqiang He. 2020. Item tagging for information retrieval: A tripartite graph neural network based approach. In SIGIR. 2327-2336.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_38_1","first-page":"732","article-title":"Spanning edge centrality: Large-scale computation and applications","author":"Mavroforakis Charalampos","year":"2015","unstructured":"Charalampos Mavroforakis, Richard Garcia-Lebron, Ioannis Koutis, and Evimaria Terzi. 2015. Spanning edge centrality: Large-scale computation and applications. In TheWebConf. 732-742.","journal-title":"TheWebConf."},{"key":"e_1_3_2_1_39_1","first-page":"101","article-title":"Topic modeling with network regularization","author":"Mei Qiaozhu","year":"2008","unstructured":"Qiaozhu Mei, Deng Cai, Duo Zhang, and ChengXiang Zhai. 2008. Topic modeling with network regularization. In TheWebConf. 101-110.","journal-title":"TheWebConf."},{"key":"e_1_3_2_1_40_1","volume-title":"NeurIPS","volume":"26","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. NeurIPS, Vol. 26 (2013)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.01.040"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1103"},{"key":"e_1_3_2_1_43_1","volume-title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint arXiv:1908.10084","author":"Reimers N","year":"2019","unstructured":"N Reimers. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint arXiv:1908.10084 (2019)."},{"key":"e_1_3_2_1_44_1","volume-title":"Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval","author":"Robertson Stephen E","unstructured":"Stephen E Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR. Springer, 232-241."},{"key":"e_1_3_2_1_45_1","volume-title":"Glu variants improve transformer. arXiv preprint arXiv:2002.05202","author":"Shazeer Noam","year":"2020","unstructured":"Noam Shazeer. 2020. Glu variants improve transformer. arXiv preprint arXiv:2002.05202 (2020)."},{"key":"e_1_3_2_1_46_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)."},{"key":"e_1_3_2_1_47_1","volume-title":"Attention is all you need. NeurIPS","author":"Vaswani A","year":"2017","unstructured":"A Vaswani. 2017. Attention is all you need. NeurIPS (2017)."},{"key":"e_1_3_2_1_48_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_49_1","first-page":"5","article-title":"Sentiment Analysis of Twitter Data","volume":"139","author":"Vishal A","year":"2016","unstructured":"A Vishal and SS Sonawane. 2016. Sentiment Analysis of Twitter Data: A Survey of Techniques. IJCA, Vol. 139, 11 (2016), 5-15.","journal-title":"A Survey of Techniques. IJCA"},{"key":"e_1_3_2_1_50_1","volume-title":"Fine-grained spoiler detection from large-scale review corpora. arXiv preprint arXiv:1905.13416","author":"Wan Mengting","year":"2019","unstructured":"Mengting Wan, Rishabh Misra, Ndapa Nakashole, and Julian McAuley. 2019. Fine-grained spoiler detection from large-scale review corpora. arXiv preprint arXiv:1905.13416 (2019)."},{"key":"e_1_3_2_1_51_1","first-page":"3638","article-title":"Bipartite dynamic representations for abuse detection","author":"Wang Andrew Z","year":"2021","unstructured":"Andrew Z Wang, Rex Ying, Pan Li, Nikhil Rao, Karthik Subbian, and Jure Leskovec. 2021b. Bipartite dynamic representations for abuse detection. In SIGKDD. 3638-3648.","journal-title":"SIGKDD."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3391298","article-title":"Edge2vec: Edge-based social network embedding","volume":"14","author":"Wang Changping","year":"2020","unstructured":"Changping Wang, Chaokun Wang, Zheng Wang, Xiaojun Ye, and Philip S Yu. 2020b. Edge2vec: Edge-based social network embedding. TKDD, Vol. 14, 4 (2020), 1-24.","journal-title":"TKDD"},{"key":"e_1_3_2_1_53_1","first-page":"2326","article-title":"Efficient and effective edge-wise graph representation learning","author":"Wang Hewen","year":"2023","unstructured":"Hewen Wang, Renchi Yang, Keke Huang, and Xiaokui Xiao. 2023. Efficient and effective edge-wise graph representation learning. In SIGKDD. 2326-2336.","journal-title":"SIGKDD."},{"key":"e_1_3_2_1_54_1","volume-title":"SAFT: Structure-aware Transformers for Textual Interaction Classification. arXiv preprint arXiv:2504.04861","author":"Wang Hongtao","year":"2025","unstructured":"Hongtao Wang, Renchi Yang, Hewen Wang, Haoran Zheng, and Jianliang Xu. 2025. SAFT: Structure-aware Transformers for Textual Interaction Classification. arXiv preprint arXiv:2504.04861 (2025)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"crossref","unstructured":"Hewen Wang Renchi Yang and Xiaokui Xiao. 2024. Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs. In SIGKDD.","DOI":"10.1145\/3637528.3671805"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.6339\/22-JDS1047"},{"key":"e_1_3_2_1_57_1","first-page":"2144","article-title":"A generic edge-empowered graph convolutional network via node-edge mutual enhancement","author":"Wang Pengyang","year":"2020","unstructured":"Pengyang Wang, Jiaping Gui, Zhengzhang Chen, Junghwan Rhee, Haifeng Chen, and Yanjie Fu. 2020a. A generic edge-empowered graph convolutional network via node-edge mutual enhancement. In TheWebConf. 2144-2154.","journal-title":"TheWebConf."},{"key":"e_1_3_2_1_58_1","first-page":"3148","article-title":"Layer-Assisted Neural Topic Modeling over Document Networks","author":"Wang Yiming","year":"2021","unstructured":"Yiming Wang, Ximing Li, and Jihong Ouyang. 2021a. Layer-Assisted Neural Topic Modeling over Document Networks.. In IJCAI. 3148-3154.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_59_1","first-page":"6861","article-title":"Simplifying graph convolutional networks","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. PMLR, 6861-6871.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_60_1","first-page":"4","volume-title":"TNNLS","volume":"32","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. TNNLS, Vol. 32, 1 (2020), 4-24."},{"key":"e_1_3_2_1_61_1","first-page":"2604","article-title":"Graph relational topic model with higher-order graph attention auto-encoders","author":"Xie Qianqian","year":"2021","unstructured":"Qianqian Xie, Jimin Huang, Pan Du, and Min Peng. 2021. Graph relational topic model with higher-order graph attention auto-encoders. In ACL-IJCNLP. 2604-2613.","journal-title":"ACL-IJCNLP."},{"key":"e_1_3_2_1_62_1","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In ICML. PMLR, 5453-5462.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_63_1","first-page":"28798","article-title":"Graphformers: Gnn-nested transformers for representation learning on textual graph","volume":"34","author":"Yang Junhan","year":"2021","unstructured":"Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, and Xing Xie. 2021. Graphformers: Gnn-nested transformers for representation learning on textual graph. NeurIPS, Vol. 34 (2021), 28798-28810.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_64_1","volume-title":"Predicting missing links in complex networks based on common neighbors and distance. Scientific reports","author":"Yang Jinxuan","year":"2016","unstructured":"Jinxuan Yang and Xiao-Dong Zhang. 2016. Predicting missing links in complex networks based on common neighbors and distance. Scientific reports, Vol. 6, 1 (2016), 1-10."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511959"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588696"},{"key":"e_1_3_2_1_67_1","volume-title":"MRFS: Mining Rating Fraud Subgraph in Bipartite Graph for Users and Products. TCSS","author":"Yu Wei","year":"2023","unstructured":"Wei Yu, Wenkai Wang, Guangquan Xu, Huaming Wu, Hongyan Li, Jun Wang, Xiaoming Li, and Juan Liu. 2023. MRFS: Mining Rating Fraud Subgraph in Bipartite Graph for Users and Products. TCSS (2023)."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6152"},{"key":"e_1_3_2_1_69_1","volume-title":"Topic Modeling on Document Networks with Dirichlet Optimal Transport Barycenter. TKDE","author":"Zhang Delvin Ce","year":"2023","unstructured":"Delvin Ce Zhang and Hady W Lauw. 2023. Topic Modeling on Document Networks with Dirichlet Optimal Transport Barycenter. TKDE (2023)."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599323"},{"key":"e_1_3_2_1_71_1","volume-title":"Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709","author":"Zhao Jianan","year":"2022","unstructured":"Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang. 2022. Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709 (2022)."},{"key":"e_1_3_2_1_72_1","volume-title":"GEAR: Graph-based evidence aggregating and reasoning for fact verification. arXiv preprint arXiv:1908.01843","author":"Zhou Jie","year":"2019","unstructured":"Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2019. GEAR: Graph-based evidence aggregating and reasoning for fact verification. arXiv preprint arXiv:1908.01843 (2019)."},{"key":"e_1_3_2_1_73_1","first-page":"2848","article-title":"Textgnn: Improving text encoder via graph neural network in sponsored search","author":"Zhu Jason","year":"2021","unstructured":"Jason Zhu, Yanling Cui, Yuming Liu, Hao Sun, Xue Li, Markus Pelger, Tianqi Yang, Liangjie Zhang, Ruofei Zhang, and Huasha Zhao. 2021. Textgnn: Improving text encoder via graph neural network in sponsored search. In TheWebConf. 2848-2857.","journal-title":"TheWebConf."}],"event":{"name":"SIGIR '25: The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Padua Italy","acronym":"SIGIR '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3726302.3730079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T18:34:40Z","timestamp":1755887680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3726302.3730079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,13]]},"references-count":73,"alternative-id":["10.1145\/3726302.3730079","10.1145\/3726302"],"URL":"https:\/\/doi.org\/10.1145\/3726302.3730079","relation":{},"subject":[],"published":{"date-parts":[[2025,7,13]]},"assertion":[{"value":"2025-07-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}