{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:46:20Z","timestamp":1777873580860,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","funder":[{"name":"Provincial Key Research and Development Program of Anhui","award":["202423l10050033"],"award-info":[{"award-number":["202423l10050033"]}]},{"DOI":"10.13039\/501100006374","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3105405"],"award-info":[{"award-number":["2022YFB3105405"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736862","type":"proceedings-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:30:13Z","timestamp":1754055013000},"page":"860-871","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5113-9515","authenticated-orcid":false,"given":"Buyun","family":"He","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1658-7306","authenticated-orcid":false,"given":"Xiaorui","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4458-2731","authenticated-orcid":false,"given":"Qi","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7079-9754","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2473-6229","authenticated-orcid":false,"given":"Yingguang","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6403-0557","authenticated-orcid":false,"given":"Yong","family":"Liao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11135-018-0777-7"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3409116"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20314"},{"key":"e_1_3_2_2_6_1","first-page":"35254","volume-title":"NIPS","volume":"35","author":"Feng Shangbin","year":"2022","unstructured":"Shangbin Feng, Zhaoxuan Tan, Herun Wan, Ningnan Wang, Zilong Chen, Binchi Zhang, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, et al., 2022b. TwiBot-22: Towards graph-based Twitter bot detection. NIPS, Vol. 35 (2022), 35254-35269."},{"key":"e_1_3_2_2_7_1","first-page":"4485","article-title":"Twibot-20: A comprehensive twitter bot detection benchmark","author":"Feng Shangbin","year":"2021","unstructured":"Shangbin Feng, Herun Wan, Ningnan Wang, Jundong Li, and Minnan Luo. 2021b. Twibot-20: A comprehensive twitter bot detection benchmark. In CIKM. 4485-4494.","journal-title":"CIKM."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487351.3488336"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. arXiv preprint arXiv:1707.00086(2017).","DOI":"10.5210\/fm.v22i8.8005"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Emilio Ferrara. 2022. Twitter spam and false accounts prevalence detection and characterization: A survey. arXiv preprint arXiv:2211.05913(2022).","DOI":"10.5210\/fm.v27i12.12872"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Emilio Ferrara Herbert Chang Emily Chen Goran Muric and Jaimin Patel. 2020. Characterizing social media manipulation in the 2020 US presidential election. First Monday(2020).","DOI":"10.5210\/fm.v25i11.11431"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47874-6_3"},{"key":"e_1_3_2_2_13_1","unstructured":"Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428(2019)."},{"key":"e_1_3_2_2_14_1","volume-title":"Maskvit: Masked visual pre-training for video prediction. arXiv preprint arXiv:2206.11894(2022).","author":"Gupta Agrim","year":"2022","unstructured":"Agrim Gupta, Stephen Tian, Yunzhi Zhang, Jiajun Wu, Roberto Mart\u00edn-Mart\u00edn, and Li Fei-Fei. 2022. Maskvit: Masked visual pre-training for video prediction. arXiv preprint arXiv:2206.11894(2022)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"John A Hartigan Manchek A Wong et al. 1979. A k-means clustering algorithm. Applied statistics Vol. 28 1 (1979) 100-108.","DOI":"10.2307\/2346830"},{"key":"e_1_3_2_2_16_1","first-page":"3660","article-title":"Simplistic collection and labeling practices limit the utility of benchmark datasets for Twitter bot detection","author":"Hays Chris","year":"2023","unstructured":"Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, and Philipp Zimmer. 2023. Simplistic collection and labeling practices limit the utility of benchmark datasets for Twitter bot detection. In WWW. 3660-3669.","journal-title":"WWW."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/646"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681302"},{"key":"e_1_3_2_2_20_1","unstructured":"Thomas N Kipf and Max Welling. 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016)."},{"key":"e_1_3_2_2_21_1","unstructured":"Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308(2016)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599546"},{"key":"e_1_3_2_2_23_1","first-page":"13242","article-title":"Finding global homophily in graph neural networks when meeting heterophily","author":"Li Xiang","year":"2022","unstructured":"Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. 2022. Finding global homophily in graph neural networks when meeting heterophily. In ICML. PMLR, 13242-13256.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_2_24_1","volume-title":"Multi-view Graph Representation Learning Beyond Homophily. ACM Transactions on Knowledge Discovery from Data","author":"Lin Bei","year":"2023","unstructured":"Bei Lin, You Li, Ning Gui, Zhuopeng Xu, and Zhiwu Yu. 2023. Multi-view Graph Representation Learning Beyond Homophily. ACM Transactions on Knowledge Discovery from Data, Vol. 17, 8 (2023), 1-21."},{"key":"e_1_3_2_2_25_1","volume-title":"Graph self-supervised learning: A survey","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and S Yu Philip. 2022. Graph self-supervised learning: A survey. IEEE transactions on knowledge and data engineering, Vol. 35, 6 (2022), 5879-5900."},{"key":"e_1_3_2_2_26_1","first-page":"485","article-title":"Botmoe: Twitter bot detection with community-aware mixtures of modal-specific experts","author":"Liu Yuhan","year":"2023","unstructured":"Yuhan Liu, Zhaoxuan Tan, Heng Wang, Shangbin Feng, Qinghua Zheng, and Minnan Luo. 2023. Botmoe: Twitter bot detection with community-aware mixtures of modal-specific experts. In SIGIR. 485-495.","journal-title":"SIGIR."},{"key":"e_1_3_2_2_27_1","unstructured":"Sitao Luan Chenqing Hua Qincheng Lu Liheng Ma Lirong Wu Xinyu Wang Minkai Xu Xiao-Wen Chang Doina Precup Rex Ying et al. 2024. The heterophilic graph learning handbook: Benchmarks models theoretical analysis applications and challenges. arXiv preprint arXiv:2407.09618(2024)."},{"key":"e_1_3_2_2_28_1","first-page":"1362","volume-title":"NIPS","volume":"35","author":"Luan Sitao","year":"2022","unstructured":"Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. 2022. Revisiting heterophily for graph neural networks. NIPS, Vol. 35 (2022), 1362-1375."},{"key":"e_1_3_2_2_29_1","volume-title":"Visualizing Data using t-SNE. Journal of Machine Learning Research,Journal of Machine Learning Research(Jan","author":"Maaten Laurensvander","year":"2008","unstructured":"Laurensvander Maaten and GeoffreyE. Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research,Journal of Machine Learning Research(Jan 2008)."},{"key":"e_1_3_2_2_30_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Sippo Rossi Matti Rossi Bikesh Upreti and Yong Liu. 2020. Detecting political bots on Twitter during the 2019 Finnish parliamentary election. (2020).","DOI":"10.24251\/HICSS.2020.298"},{"key":"e_1_3_2_2_32_1","volume-title":"Mgtab: A multi-relational graph-based twitter account detection benchmark. arXiv preprint arXiv:2301.01123(2023).","author":"Shi Shuhao","year":"2023","unstructured":"Shuhao Shi, Kai Qiao, Jian Chen, Shuai Yang, Jie Yang, Baojie Song, Linyuan Wang, and Bin Yan. 2023. Mgtab: A multi-relational graph-based twitter account detection benchmark. arXiv preprint arXiv:2301.01123(2023)."},{"key":"e_1_3_2_2_33_1","volume-title":"Mgae: Masked autoencoders for self-supervised learning on graphs. arXiv preprint arXiv:2201.02534(2022).","author":"Tan Qiaoyu","year":"2022","unstructured":"Qiaoyu Tan, Ninghao Liu, Xiao Huang, Rui Chen, Soo-Hyun Choi, and Xia Hu. 2022. Mgae: Masked autoencoders for self-supervised learning on graphs. arXiv preprint arXiv:2201.02534(2022)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26192"},{"key":"e_1_3_2_2_35_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_36_1","first-page":"280","article-title":"Online human-bot interactions: Detection, estimation, and characterization","volume":"11","author":"Varol Onur","year":"2017","unstructured":"Onur Varol, Emilio Ferrara, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online human-bot interactions: Detection, estimation, and characterization. In AAAI, Vol. 11. 280-289.","journal-title":"AAAI"},{"key":"e_1_3_2_2_37_1","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_2_38_1","first-page":"3364","article-title":"Generative and contrastive paradigms are complementary for graph self-supervised learning","author":"Wang Yuxiang","year":"2024","unstructured":"Yuxiang Wang, Xiao Yan, Chuang Hu, Quanqing Xu, Chuanhui Yang, Fangcheng Fu, Wentao Zhang, Hao Wang, Bo Du, and Jiawei Jiang. 2024. Generative and contrastive paradigms are complementary for graph self-supervised learning. In ICDE. IEEE, 3364-3378.","journal-title":"ICDE. IEEE"},{"key":"e_1_3_2_2_39_1","volume-title":"Homophily and Transitivity in Bot Disinformation Networks. In 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 1-7.","author":"Williams Evan M","year":"2020","unstructured":"Evan M Williams, Valerie Novak, Dylan Blackwell, Paul Platzman, Ian McCulloh, and Nolan Edward Phillips. 2020. Homophily and Transitivity in Bot Disinformation Networks. In 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 1-7."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Qi Wu Yingguang Yang Buyun He Hao Liu Xiang Wang Yong Liao Renyu Yang and Pengyuan Zhou. 2023. Heterophily-aware social bot detection with supervised contrastive learning. arXiv preprint arXiv:2306.07478(2023).","DOI":"10.1007\/978-3-031-78183-4_4"},{"key":"e_1_3_2_2_41_1","volume-title":"Unsupervised deep embedding for clustering analysis. ICML(Jun","author":"Xie Jin","year":"2016","unstructured":"Jin Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. ICML(Jun 2016)."},{"key":"e_1_3_2_2_42_1","volume-title":"Self-supervised learning of graph neural networks: A unified review","author":"Xie Yaochen","year":"2022","unstructured":"Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji. 2022. Self-supervised learning of graph neural networks: A unified review. IEEE transactions on pattern analysis and machine intelligence, Vol. 45, 2 (2022), 2412-2429."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5460"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671871"},{"key":"e_1_3_2_2_45_1","first-page":"1314","article-title":"FedACK","author":"Yang Yingguang","year":"2023","unstructured":"Yingguang Yang, Renyu Yang, Hao Peng, Yangyang Li, Tong Li, Yong Liao, and Pengyuan Zhou. 2023. FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection. In WWW. 1314-1323.","journal-title":"In WWW."},{"key":"e_1_3_2_2_46_1","first-page":"321","article-title":"Graph masked autoencoder for sequential recommendation","author":"Ye Yaowen","year":"2023","unstructured":"Yaowen Ye, Lianghao Xia, and Chao Huang. 2023. Graph masked autoencoder for sequential recommendation. In SIGIR. 321-330.","journal-title":"SIGIR."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27788"},{"key":"e_1_3_2_2_48_1","first-page":"76","volume-title":"NIPS","volume":"34","author":"Zhang Hengrui","year":"2021","unstructured":"Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S Yu. 2021. From canonical correlation analysis to self-supervised graph neural networks. NIPS, Vol. 34 (2021), 76-89."},{"key":"e_1_3_2_2_49_1","unstructured":"Xin Zheng Yi Wang Yixin Liu Ming Li Miao Zhang Di Jin Philip S Yu and Shirui Pan. 2022. Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082(2022)."},{"key":"e_1_3_2_2_50_1","volume-title":"ProtoMGAE: prototype-aware masked graph auto-encoder for graph representation learning. ACM Transactions on Knowledge Discovery from Data","author":"Zheng Yimei","year":"2024","unstructured":"Yimei Zheng and Caiyan Jia. 2024. ProtoMGAE: prototype-aware masked graph auto-encoder for graph representation learning. ACM Transactions on Knowledge Discovery from Data, Vol. 18, 6 (2024), 1-22."},{"key":"e_1_3_2_2_51_1","first-page":"4995","article-title":"Detecting social bot on the fly using contrastive learning","author":"Zhou Ming","year":"2023","unstructured":"Ming Zhou, Dan Zhang, Yuandong Wang, Yangli-Ao Geng, and Jie Tang. 2023. Detecting social bot on the fly using contrastive learning. In CIKM. 4995-5001.","journal-title":"CIKM."},{"key":"e_1_3_2_2_52_1","first-page":"7793","volume-title":"NIPS","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. NIPS, Vol. 33 (2020), 7793-7804."}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:04:10Z","timestamp":1777572250000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":52,"alternative-id":["10.1145\/3711896.3736862","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736862","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}