{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T12:57:43Z","timestamp":1778158663213,"version":"3.51.4"},"reference-count":63,"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"}]},{"name":"S&T Program of Hebei","award":["20310101D"],"award-info":[{"award-number":["20310101D"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["U20B2053"],"award-info":[{"award-number":["U20B2053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["SQ2021YFC3300088"],"award-info":[{"award-number":["SQ2021YFC3300088"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"UK EPSRC","award":["EP\/T01461X\/1"],"award-info":[{"award-number":["EP\/T01461X\/1"]}]},{"name":"UK Turing Pilot Project"},{"name":"UK Alan Turing PDEA Scheme"}],"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 bots are referred to as the automated accounts on social networks that make attempts to behave like humans. While Graph Neural Networks (GNNs) have been massively applied to the field of social bot detection, a huge amount of domain expertise and prior knowledge is heavily engaged in the state-of-the-art approaches to design a dedicated neural network architecture for a specific classification task. Involving oversized nodes and network layers in the model design, however, usually causes the over-smoothing problem and the lack of embedding discrimination. In this article, we propose\n            <jats:sc>RoSGAS<\/jats:sc>\n            , a novel\n            <jats:underline>R<\/jats:underline>\n            einf\n            <jats:underline>o<\/jats:underline>\n            rced and\n            <jats:underline>S<\/jats:underline>\n            elf-supervised\n            <jats:underline>G<\/jats:underline>\n            NN\n            <jats:underline>A<\/jats:underline>\n            rchitecture\n            <jats:underline>S<\/jats:underline>\n            earch framework to adaptively pinpoint the most suitable multi-hop neighborhood and the number of layers in the GNN architecture. More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task. We exploit the heterogeneous information network to present the user connectivity by leveraging account metadata, relationships, behavioral features, and content features.\n            <jats:sc>RoSGAS<\/jats:sc>\n            uses a multi-agent deep reinforcement learning (RL), 31 pages. mechanism for navigating the search of optimal neighborhood and network layers to learn individually the subgraph embedding for each target user. A nearest neighbor mechanism is developed for accelerating the RL training process, and\n            <jats:sc>RoSGAS<\/jats:sc>\n            can learn more discriminative subgraph embedding with the aid of self-supervised learning. Experiments on five Twitter datasets show that\n            <jats:sc>RoSGAS<\/jats:sc>\n            outperforms the state-of-the-art approaches in terms of accuracy, training efficiency, and stability and has better generalization when handling unseen samples.\n          <\/jats:p>","DOI":"10.1145\/3572403","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T13:49:16Z","timestamp":1669988956000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":62,"title":["<scp>RoSGAS<\/scp>\n            : Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2473-6229","authenticated-orcid":false,"given":"Yingguang","family":"Yang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, China and Key Laboratory of Cyberculture Content Cognition and Detection, Ministry of Culture and Tourism, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6334-4925","authenticated-orcid":false,"given":"Renyu","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Leeds, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8478-3932","authenticated-orcid":false,"given":"Yangyang","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Risk Perception and Prevention, CAEIT, China; Key Laboratory of Cyberculture Content Cognition and Detection, Ministry of Culture and Tourism, China and Academy of Cyber, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9254-2293","authenticated-orcid":false,"given":"Kai","family":"Cui","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China, Key Laboratory of Cyberculture Content Cognition and Detection, Ministry of Culture and Tourism, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7047-2981","authenticated-orcid":false,"given":"Zhiqin","family":"Yang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4608-2852","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4102-233X","authenticated-orcid":false,"given":"Jie","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Leeds, United Kingdom, and Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2084-2697","authenticated-orcid":false,"given":"Haiyong","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China, Key Laboratory of Cyberculture Content Cognition and Detection, Ministry of Culture and Tourism, China"}]}],"member":"320","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1145\/2675133.2675208","volume-title":"CSCW","author":"Abokhodair Norah","year":"2015","unstructured":"Norah Abokhodair, Daisy Yoo, and David W. McDonald. 2015. Dissecting a social botnet: Growth, content and influence in Twitter. CSCW. 839\u2013851."},{"key":"e_1_3_2_3_2","first-page":"148","volume-title":"WWW","author":"Alhosseini Seyed Ali","year":"2019","unstructured":"Seyed Ali Alhosseini, Raad Bin Tareaf, Pejman Najafi, and Christoph Meinel. 2019. Detect me if you can: Spam bot detection using inductive representation learning. WWW. 148\u2013153."},{"key":"e_1_3_2_4_2","first-page":"8017","article-title":"Subgraph neural networks","volume":"33","author":"Alsentzer Emily","year":"2020","unstructured":"Emily Alsentzer, Samuel Finlayson, Michelle Li, and Marinka Zitnik. 2020. Subgraph neural networks. NIPS 33 (2020), 8017\u20138029.","journal-title":"NIPS"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.509"},{"key":"e_1_3_2_6_2","article-title":"Graph neural networks with convolutional ARMAfilters","author":"Bianchi Filippo Maria","year":"2021","unstructured":"Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, and Cesare Alippi. 2021. Graph neural networks with convolutional ARMAfilters. TPAMI (2021).","journal-title":"TPAMI"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1145\/3366423.3380204","volume-title":"WWW","author":"Breuer Adam","year":"2020","unstructured":"Adam Breuer, Roee Eilat, and Udi Weinsberg. 2020. Friend or faux: Graph-based early detection of fake accounts on social networks. WWW. 1287\u20131297."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3409116"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2015.09.003"},{"key":"e_1_3_2_10_2","first-page":"963","volume-title":"WWW","author":"Cresci Stefano","year":"2017","unstructured":"Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. WWW. 963\u2013972."},{"key":"e_1_3_2_11_2","volume-title":"AAAI","author":"Dai Quanyu","year":"2018","unstructured":"Quanyu Dai, Qiang Li, Jian Tang, and Dan Wang. 2018. Adversarial network embedding. In AAAI, Vol. 32."},{"key":"e_1_3_2_12_2","first-page":"4171","volume-title":"NAACL","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. NAACL. 4171\u20134186."},{"key":"e_1_3_2_13_2","first-page":"913","volume-title":"CIKM","author":"Ding Ming","year":"2018","unstructured":"Ming Ding, Jie Tang, and Jie Zhang. 2018. Semi-supervised learning on graphs with generative adversarial nets. CIKM. 913\u2013922."},{"key":"e_1_3_2_14_2","first-page":"135","volume-title":"KDD","author":"Dong Yuxiao","year":"2017","unstructured":"Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. KDD. 135\u2013144."},{"key":"e_1_3_2_15_2","first-page":"315","volume-title":"CIKM","author":"Dou Yingtong","year":"2020","unstructured":"Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S. Yu. 2020. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. CIKM. 315\u2013324."},{"key":"e_1_3_2_16_2","article-title":"Heterogeneity-aware Twitter bot detection with relational graph transformers","author":"Feng Shangbin","year":"2022","unstructured":"Shangbin Feng, Zhaoxuan Tan, Rui Li, and Minnan Luo. 2022. Heterogeneity-aware Twitter bot detection with relational graph transformers. AAAI.","journal-title":"AAAI"},{"key":"e_1_3_2_17_2","first-page":"4485","volume-title":"CIKM","author":"Feng Shangbin","year":"2021","unstructured":"Shangbin Feng, Herun Wan, Ningnan Wang, Jundong Li, and Minnan Luo. 2021. TwiBot-20: A comprehensive Twitter bot detection benchmark. CIKM. 4485\u20134494."},{"key":"e_1_3_2_18_2","first-page":"236","volume-title":"Proceedings of the 2021 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM\u201921)","author":"Feng Shangbin","year":"2021","unstructured":"Shangbin Feng, Herun Wan, Ningnan Wang, and Minnan Luo. 2021. BotRGCN: Twitter bot detection with relational graph convolutional networks. In Proceedings of the 2021 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM\u201921). 236\u2013239."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/2818717"},{"key":"e_1_3_2_20_2","volume-title":"ICLR Workshop","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast graph representation learning with PyTorch geometric. ICLR Workshop."},{"key":"e_1_3_2_21_2","first-page":"1403","volume-title":"IJCAI","author":"Gao Yang","year":"2020","unstructured":"Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020. Graph neural architecture search. IJCAI, Vol. 20. 1403\u20131409."},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1145\/3110025.3110091","volume-title":"ASONAM","author":"Gilani Zafar","year":"2017","unstructured":"Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft. 2017. Classification of Twitter accounts into automated agents and human users. ASONAM. 489\u2013496."},{"key":"e_1_3_2_23_2","first-page":"855","volume-title":"KDD","author":"Grover Aditya","year":"2016","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. KDD. 855\u2013864."},{"key":"e_1_3_2_24_2","first-page":"1025","volume-title":"NIPS","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. NIPS. 1025\u20131035."},{"key":"e_1_3_2_25_2","first-page":"1","article-title":"Hawk: Rapid android malware detection through heterogeneous graph attention networks","author":"Hei Yiming","year":"2021","unstructured":"Yiming Hei, Renyu Yang, Hao Peng, Lihong Wang, Xiaolin Xu, Jianwei Liu, Hong Liu, Jie Xu, and Lichao Sun. 2021. Hawk: Rapid android malware detection through heterogeneous graph attention networks. TNNLS (2021), 1\u201315.","journal-title":"TNNLS"},{"key":"e_1_3_2_26_2","article-title":"Variational graph auto-encoders","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).","journal-title":"arXiv preprint arXiv:1611.07308"},{"key":"e_1_3_2_27_2","volume-title":"ICLR","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR."},{"key":"e_1_3_2_28_2","first-page":"461","volume-title":"KDD","author":"Lai Kwei-Herng","year":"2020","unstructured":"Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, and Xia Hu. 2020. Policy-GNN: Aggregation optimization for graph neural networks. KDD. 461\u2013471."},{"key":"e_1_3_2_29_2","first-page":"577","volume-title":"ECCV","author":"Larsson Gustav","year":"2016","unstructured":"Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. 2016. Learning representations for automatic colorization. ECCV. Springer, 577\u2013593."},{"key":"e_1_3_2_30_2","first-page":"499","volume-title":"CIKM","author":"Lee John Boaz","year":"2019","unstructured":"John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao. 2019. Graph convolutional networks with motif-based attention. CIKM. 499\u2013508."},{"key":"e_1_3_2_31_2","volume-title":"32nd AAAI Conference on Artificial Intelligence","author":"Li Qimai","year":"2018","unstructured":"Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. 32nd AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_32_2","first-page":"2077","volume-title":"CIKM","author":"Liu Ziqi","year":"2018","unstructured":"Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. 2018. Heterogeneous graph neural networks for malicious account detection. CIKM. 2077\u20132085."},{"key":"e_1_3_2_33_2","first-page":"183","volume-title":"WebSci","author":"Mazza Michele","year":"2019","unstructured":"Michele Mazza, Stefano Cresci, Marco Avvenuti, Walter Quattrociocchi, and Maurizio Tesconi. 2019. Rtbust: Exploiting temporal patterns for botnet detection on Twitter. WebSci. 183\u2013192."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep01783"},{"key":"e_1_3_2_36_2","first-page":"5387","volume-title":"AAAI","author":"Peng Hao","year":"2020","unstructured":"Hao Peng, Jianxin Li, Qiran Gong, Yuanxin Ning, Senzhang Wang, and Lifang He. 2020. Motif-matching based subgraph-level attentional convolutional network for graph classification. AAAI, Vol. 34. 5387\u20135394."},{"key":"e_1_3_2_37_2","first-page":"628","article-title":"Lime: Low-cost incremental learning for dynamic heterogeneous information networks","author":"Peng Hao","year":"2021","unstructured":"Hao Peng, Renyu Yang, Zheng Wang, Jianxin Li, Lifang He, Philip Yu, Albert Zomaya, and Raj Ranjan. 2021. Lime: Low-cost incremental learning for dynamic heterogeneous information networks. IEEE Trans. Comput. (2021), 628\u2013642.","journal-title":"IEEE Trans. Comput."},{"issue":"4","key":"e_1_3_2_38_2","first-page":"69","article-title":"Reinforced neighborhood selection guided multi-relational graph neural networks","volume":"40","author":"Peng Hao","year":"2021","unstructured":"Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, and Philip S. Yu. 2021. Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans. Inf. Syst. 40, 4, Article 69 (Dec.2021), 46 pages.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3144993"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1145\/3366423.3380112","volume-title":"WWW","author":"Peng Zhen","year":"2020","unstructured":"Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang. 2020. Graph representation learning via graphical mutual information maximization. WWW. 259\u2013270."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_42_2","first-page":"9558","volume-title":"AAAI","author":"Shen Junhong","year":"2021","unstructured":"Junhong Shen and Lin F. Yang. 2021. Theoretically principled deep RL acceleration via nearest neighbor function approximation. AAAI, Vol. 35. 9558\u20139566."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"e_1_3_2_44_2","unstructured":"Fan-Yun Sun Jordan Hoffmann Vikas Verma and Jian Tang. 2019. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019)."},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1145\/2736277.2741093","volume-title":"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. WWW. 1067\u20131077."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/321921.321925"},{"key":"e_1_3_2_47_2","first-page":"4790","volume-title":"NIPS","author":"Oord A\u00e4ron van den","year":"2016","unstructured":"A\u00e4ron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, and Alex Graves. 2016. Conditional image generation with PixelCNN decoders. NIPS. 4790\u20134798."},{"key":"e_1_3_2_48_2","volume-title":"Proceedings of the International AAAI Conference on Web and Social Media (ICWSM\u201917)","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 Proceedings of the International AAAI Conference on Web and Social Media (ICWSM\u201917), Vol. 11."},{"key":"e_1_3_2_49_2","volume-title":"ICLR","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. ICLR."},{"issue":"3","key":"e_1_3_2_50_2","first-page":"4","article-title":"Deep graph infomax.","volume":"2","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic, William Fedus, William L. Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R. Devon Hjelm. 2019. Deep graph infomax. ICLR (Poster) 2, 3 (2019), 4.","journal-title":"ICLR (Poster)"},{"key":"e_1_3_2_51_2","first-page":"310","volume-title":"WWW","author":"Wang Jianyu","year":"2019","unstructured":"Jianyu Wang, Rui Wen, Chunming Wu, Yu Huang, and Jian Xion. 2019. Fdgars: Fraudster detection via graph convolutional networks in online app review system. WWW. 310\u2013316."},{"key":"e_1_3_2_52_2","first-page":"6861","volume-title":"ICML","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. ICML. 6861\u20136871."},{"key":"e_1_3_2_53_2","first-page":"564","volume-title":"EMNLP","author":"Xiong Wenhan","year":"2017","unstructured":"Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017. Deeppath: A reinforcement learning method for knowledge graph reasoning. EMNLP. 564\u2013573."},{"key":"e_1_3_2_54_2","first-page":"5453","volume-title":"ICML","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. ICML. PMLR, 5453\u20135462."},{"key":"e_1_3_2_55_2","first-page":"47","volume-title":"ASONAM","author":"Yang Carl","year":"2018","unstructured":"Carl Yang, Mengxiong Liu, Vincent W. Zheng, and Jiawei Han. 2018. Node, motif and subgraph: Leveraging network functional blocks through structural convolution. ASONAM. IEEE, 47\u201352."},{"issue":"1","key":"e_1_3_2_56_2","first-page":"48","article-title":"Arming the public with artificial intelligence to counter social bots","volume":"1","author":"Yang Kai-Cheng","year":"2019","unstructured":"Kai-Cheng Yang, Onur Varol, Clayton A. Davis, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. 2019. Arming the public with artificial intelligence to counter social bots. Comput. Hum. Behav. 1, 1 (2019), 48\u201361.","journal-title":"Comput. Hum. Behav."},{"key":"e_1_3_2_57_2","first-page":"1096","volume-title":"AAAI","author":"Yang Kai-Cheng","year":"2020","unstructured":"Kai-Cheng Yang, Onur Varol, Pik-Mai Hui, and Filippo Menczer. 2020. Scalable and generalizable social bot detection through data selection. AAAI, Vol. 34. 1096\u20131103."},{"key":"e_1_3_2_58_2","first-page":"1417","volume-title":"IJCAI","author":"Yang Xiaoyu","year":"2020","unstructured":"Xiaoyu Yang, Yuefei Lyu, Tian Tian, Yifei Liu, Yudong Liu, and Xi Zhang. 2020. Rumor detection on social media with graph structured adversarial learning. IJCAI. 1417\u20131423."},{"key":"e_1_3_2_59_2","first-page":"178","volume-title":"BlockSys","author":"Yuan Zihao","year":"2020","unstructured":"Zihao Yuan, Qi Yuan, and Jiajing Wu. 2020. Phishing detection on ethereum via learning representation of transaction subgraphs. BlockSys. Springer, 178\u2013191."},{"key":"e_1_3_2_60_2","volume-title":"ICLR","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020. Graphsaint: Graph sampling based inductive learning method. ICLR."},{"key":"e_1_3_2_61_2","first-page":"5059","volume-title":"ACL","author":"Zhang Xingxing","year":"2019","unstructured":"Xingxing Zhang, Furu Wei, and Ming Zhou. 2019. HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization. ACL. 5059\u20135069."},{"key":"e_1_3_2_62_2","first-page":"1","article-title":"Multi-view tensor graph neural networks through reinforced aggregation","author":"Zhao Xusheng","year":"2022","unstructured":"Xusheng Zhao, Qiong Dai, Jia Wu, Hao Peng, Mingsheng Liu, Xu Bai, Jianlong Tang, and Philip S. Yu. 2022. Multi-view tensor graph neural networks through reinforced aggregation. TKDE (2022), 1\u201314.","journal-title":"TKDE"},{"key":"e_1_3_2_63_2","article-title":"Reinforcement learning enhanced heterogeneous graph neural network","author":"Zhong Zhiqiang","year":"2020","unstructured":"Zhiqiang Zhong, Cheng-Te Li, and Jun Pang. 2020. Reinforcement learning enhanced heterogeneous graph neural network. arXiv preprint arXiv:2010.13735 (2020).","journal-title":"arXiv preprint arXiv:2010.13735"},{"key":"e_1_3_2_64_2","article-title":"Auto-GNN: Neural architecture search of graph neural networks","author":"Zhou Kaixiong","year":"2019","unstructured":"Kaixiong Zhou, Qingquan Song, Xiao Huang, and Xia Hu. 2019. Auto-GNN: Neural architecture search of graph neural networks. arXiv preprint arXiv:1909.03184 (2019).","journal-title":"arXiv preprint arXiv:1909.03184"}],"container-title":["ACM Transactions on the Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3572403","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3572403","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:14Z","timestamp":1750182674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3572403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":63,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,8,31]]}},"alternative-id":["10.1145\/3572403"],"URL":"https:\/\/doi.org\/10.1145\/3572403","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":"2022-01-25","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"}}]}}