{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:17:06Z","timestamp":1765502226287,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","funder":[{"name":"the National Key R&D Program of China","award":["2023YFC3305303"],"award-info":[{"award-number":["2023YFC3305303"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761516","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T01:03:42Z","timestamp":1762563822000},"page":"5592-5599","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Let Topology Speak: Graph Neural Network with Topology-Aware Augmentation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1682-959X","authenticated-orcid":false,"given":"Kangzhuo","family":"Chen","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Science, Beijing, China and State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7951-2548","authenticated-orcid":false,"given":"Xiaoqian","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1081-8119","authenticated-orcid":false,"given":"Huawei","family":"Shen","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Science, Beijing, China and State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5201-8195","authenticated-orcid":false,"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Science, Beijing, China and State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Networks in finance. The network challenge: strategy, profit, and risk in an interlinked world","author":"Allen Franklin","year":"2009","unstructured":"Franklin Allen and Ana Babus. 2009. Networks in finance. The network challenge: strategy, profit, and risk in an interlinked world, Vol. 367 (2009)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539129"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12805-4_6"},{"key":"e_1_3_2_1_4_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral Networks and Locally Connected Networks on Graphs. arXiv:1312.6203 [cs.LG]"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113155"},{"key":"e_1_3_2_1_6_1","first-page":"5822","article-title":"Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation","author":"Cheng Dawei","year":"2019","unstructured":"Dawei Cheng, Yi Tu, Zhen-Wei Ma, Zhibin Niu, and Liqing Zhang. 2019. Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation. In IJCAI. 5822-5828.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_7_1","volume-title":"Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks. arXiv preprint arXiv:2012.01933","author":"Feng Bojing","year":"2020","unstructured":"Bojing Feng, Haonan Xu, Wenfang Xue, and Bindang Xue. 2020. Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks. arXiv preprint arXiv:2012.01933 (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025-1035","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025-1035."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 659-668","author":"Jin B.","key":"e_1_3_2_1_13_1","unstructured":"B. Jin, C. Gao, X. He, D. Jin, and Y. Li. 2020. Multi-Behavior Recommendation with Graph Convolutional Networks. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 659-668."},{"key":"e_1_3_2_1_14_1","first-page":"2414","volume-title":"Proceedings of the Web Conference","author":"Jing B.","year":"2021","unstructured":"B. Jing, C. Park, and H. Tong. 2021. HDMI: High-order Deep Multiplex Infomax. In Proceedings of the Web Conference 2021. 2414-2424."},{"key":"e_1_3_2_1_15_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_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671765"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467350"},{"key":"e_1_3_2_1_18_1","unstructured":"Yao Ma Xiaorui Liu Neil Shah and Jiliang Tang. 2023. Is Homophily a Necessity for Graph Neural Networks? arXiv:2106.06134 [cs.LG] https:\/\/arxiv.org\/abs\/2106.06134"},{"key":"e_1_3_2_1_19_1","volume-title":"Deep learning models for bankruptcy prediction using textual disclosures. European journal of operational research","author":"Mai Feng","year":"2019","unstructured":"Feng Mai, Shaonan Tian, Chihoon Lee, and Ling Ma. 2019. Deep learning models for bankruptcy prediction using textual disclosures. European journal of operational research, Vol. 274, 2 (2019), 743-758."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Qiheng Mao Zemin Liu Chenghao Liu and Jianling Sun. 2023. HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer. arXiv:2302.11329 [cs.LG] https:\/\/arxiv.org\/abs\/2302.11329","DOI":"10.1145\/3543507.3583493"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939751"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICKG63256.2024.00041"},{"key":"e_1_3_2_1_24_1","first-page":"9368","volume-title":"Relation-aware Graph Attention Model with Adaptive Self-adversarial Training. In Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Qin X.","unstructured":"X. Qin, N. Sheikh, B. Reinwald, and L. Wu. 2021. Relation-aware Graph Attention Model with Adaptive Self-adversarial Training. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 9368-9376."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2022.3186158"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186120"},{"key":"e_1_3_2_1_29_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_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2022.3177455"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_32_1","first-page":"4486","volume-title":"Knowledge-enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Xia L.","unstructured":"L. Xia, C. Huang, Y. Xu, P. Dai, X. Zhang, H. Yang, J. Pei, and L. Bo. 2021. Knowledge-enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4486-4493."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517838"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3377369.3377376"},{"key":"e_1_3_2_1_35_1","first-page":"4661","article-title":"Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining","author":"Yang Shuo","year":"2020","unstructured":"Shuo Yang, Zhiqiang Zhang, Jun Zhou, Yang Wang, Wang Sun, Xingyu Zhong, Yanming Fang, Quan Yu, and Yuan Qi. 2020b. Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining. In IJCAI. 4661-4667.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_36_1","first-page":"11960","article-title":"Graph Transformer Networks","author":"Yun Seongjun","year":"2019","unstructured":"Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2019. Graph Transformer Networks. In Advances in Neural Information Processing Systems. 11960-11970.","journal-title":"Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219969"},{"key":"e_1_3_2_1_38_1","volume-title":"Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 140-151","author":"Zheng Yizhen","year":"2021","unstructured":"Yizhen Zheng, Vincent Lee, Zonghan Wu, and Shirui Pan. 2021. Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 140-151."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Kaixiong Zhou Qingquan Song Xiao Huang Daochen Zha Na Zou and Xia Hu. 2019. Multi-Channel Graph Convolutional Networks. arXiv:1912.08306 [cs.SI]","DOI":"10.24963\/ijcai.2020\/188"},{"key":"e_1_3_2_1_40_1","volume-title":"HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning. arXiv:2407.13158 [cs.LG] https:\/\/arxiv.org\/abs\/2407.13158","author":"Zhu Qiuyu","year":"2024","unstructured":"Qiuyu Zhu, Liang Zhang, Qianxiong Xu, Kaijun Liu, Cheng Long, and Xiaoyang Wang. 2024. HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning. arXiv:2407.13158 [cs.LG] https:\/\/arxiv.org\/abs\/2407.13158"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761516","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:16:03Z","timestamp":1765502163000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761516"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":40,"alternative-id":["10.1145\/3746252.3761516","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761516","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}