{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:09:28Z","timestamp":1765339768404,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3755334","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T06:54:15Z","timestamp":1761375255000},"page":"6540-6548","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1099-1914","authenticated-orcid":false,"given":"Mingkang","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3400-4061","authenticated-orcid":false,"given":"Xuexiong","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6101-260X","authenticated-orcid":false,"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, WuHan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0535-4928","authenticated-orcid":false,"given":"Yaoyang","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3749-4043","authenticated-orcid":false,"given":"Fu","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_2_1","unstructured":"Dexiong Chen Leslie O'Bray and Karsten Borgwardt. 2022. Structure-aware transformer for graph representation learning. In ICML. PMLR 3469--3489."},{"volume-title":"Deep anomaly detection on attributed networks","author":"Ding Kaize","key":"e_1_3_2_1_3_1","unstructured":"Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. 2019. Deep anomaly detection on attributed networks. In SDM. SIAM, 594--602."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.06.039"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25907"},{"key":"e_1_3_2_1_6_1","first-page":"1","article-title":"Hyperspectral anomaly detection with robust graph autoencoders","volume":"60","author":"Fan Ganghui","year":"2021","unstructured":"Ganghui Fan, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, and Jiayi Ma. 2021. Hyperspectral anomaly detection with robust graph autoencoders. IEEE Transactions on Geoscience and Remote Sensing 60 (2021), 1--14.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_7_1","volume-title":"Anomalydae: Dual autoencoder for anomaly detection on attributed networks","author":"Fan Haoyi","year":"2020","unstructured":"Haoyi Fan, Fengbin Zhang, and Zuoyong Li. 2020. Anomalydae: Dual autoencoder for anomaly detection on attributed networks. In ICASSP. IEEE, 5685--5689."},{"volume-title":"TransGAD: A Transformer-Based Autoencoder for Graph Anomaly Detection","author":"Guo Zehao","key":"e_1_3_2_1_8_1","unstructured":"Zehao Guo, Nannan Wu, Yiming Zhao, and Wenjun Wang. 2024. TransGAD: A Transformer-Based Autoencoder for Graph Anomaly Detection. In DASFAA. Springer, 269--284."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28691"},{"volume-title":"Unsupervised graph outlier detection: Problem revisit, new insight, and superior method","author":"Huang Yihong","key":"e_1_3_2_1_10_1","unstructured":"Yihong Huang, Liping Wang, Fan Zhang, and Xuemin Lin. 2023. Unsupervised graph outlier detection: Problem revisit, new insight, and superior method. In ICDE. IEEE, 2565--2578."},{"key":"e_1_3_2_1_11_1","volume-title":"Edge-augmented graph transformers: Global self-attention is enough for graphs. arXiv preprint arXiv:2108.03348 3","author":"Hussain Md Shamim","year":"2021","unstructured":"Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian. 2021. Edge-augmented graph transformers: Global self-attention is enough for graphs. arXiv preprint arXiv:2108.03348 3 (2021)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482057"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Parisa Kaghazgaran James Caverlee and Anna Squicciarini. 2018. Combating crowdsourced review manipulators: A neighborhood-based approach. In WSDM. 306--314.","DOI":"10.1145\/3159652.3159726"},{"key":"e_1_3_2_1_14_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 MaxWelling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Srijan Kumar Xikun Zhang and Jure Leskovec. 2019. Predicting dynamic embedding trajectory in temporal interaction networks. In SIGKDD. 1269--1278.","DOI":"10.1145\/3292500.3330895"},{"volume-title":"Financial crime & fraud detection using graph computing: Application considerations & outlook","author":"Kurshan Eren","key":"e_1_3_2_1_16_1","unstructured":"Eren Kurshan, Hongda Shen, and Haojie Yu. 2020. Financial crime & fraud detection using graph computing: Application considerations & outlook. In TransAI. IEEE, 125--130."},{"key":"e_1_3_2_1_17_1","volume-title":"Diffgad: A diffusion-based unsupervised graph anomaly detector. arXiv preprint arXiv:2410.06549","author":"Li Jinghan","year":"2024","unstructured":"Jinghan Li, Yuan Gao, Jinda Lu, Junfeng Fang, CongcongWen, Hui Lin, and Xiang Wang. 2024. Diffgad: A diffusion-based unsupervised graph anomaly detector. arXiv preprint arXiv:2410.06549 (2024)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3068344"},{"volume-title":"Enhancing Multi-view Contrastive Learning for Graph Anomaly Detection","author":"Lu Qingcheng","key":"e_1_3_2_1_19_1","unstructured":"Qingcheng Lu, NannanWu, Yiming Zhao,WenjunWang, and Quannan Zu. 2024. Enhancing Multi-view Contrastive Learning for Graph Anomaly Detection. In DASFAA. Springer, 236--251."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498389"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Hyunjong Park Jongyoun Noh and Bumsub Ham. 2020. Learning memoryguided normality for anomaly detection. In CVPR. 14372--14381.","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115742"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Shebuti Rayana and Leman Akoglu. 2015. Collective opinion spam detection: Bridging review networks and metadata. In SIGKDD. 985--994.","DOI":"10.1145\/2783258.2783370"},{"key":"e_1_3_2_1_25_1","volume-title":"Gad-nr: Graph anomaly detection via neighborhood reconstruction. In WSDM. 576--585.","author":"Roy Amit","year":"2024","unstructured":"Amit Roy, Juan Shu, Jia Li, Carl Yang, Olivier Elshocht, Jeroen Smeets, and Pan Li. 2024. Gad-nr: Graph anomaly detection via neighborhood reconstruction. In WSDM. 576--585."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_1_27_1","volume-title":"Self-attention with relative position representations. arXiv preprint arXiv:1803.02155","author":"Shaw Peter","year":"2018","unstructured":"Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018. Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018)."},{"key":"e_1_3_2_1_28_1","unstructured":"Jianheng Tang Jiajin Li Ziqi Gao and Jia Li. 2022. Rethinking graph neural networks for anomaly detection. In ICML. PMLR 21076--21089."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In SIGKDD. 990--998.","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Lei Tang and Huan Liu. 2009. Relational learning via latent social dimensions. In SIGKDD. 817--826.","DOI":"10.1145\/1557019.1557109"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2023.102538"},{"key":"e_1_3_2_1_32_1","volume-title":"Demystifying oversmoothing in attention-based graph neural networks. Advances in Neural Information Processing Systems 36","author":"Wu Xinyi","year":"2024","unstructured":"Xinyi Wu, Amir Ajorlou, Zihui Wu, and Ali Jadbabaie. 2024. Demystifying oversmoothing in attention-based graph neural networks. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8872923"},{"key":"e_1_3_2_1_34_1","volume-title":"Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140","author":"Zhang Jiawei","year":"2020","unstructured":"Jiawei Zhang, Haopeng Zhang, Congying Xia, and Li Sun. 2020. Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140 (2020)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Tong Zhao Chuchen Deng Kaifeng Yu Tianwen Jiang DahengWang and Meng Jiang. 2020. Error-bounded graph anomaly loss for gnns. In CIKM. 1873--1882.","DOI":"10.1145\/3340531.3411979"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3119326"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Qinghai Zhou Yuzhong Chen Zhe Xu Yuhang Wu Menghai Pan Mahashweta Das Hao Yang and Hanghang Tong. 2024. Graph Anomaly Detection with Adaptive Node Mixup. In CIKM. 3494--3504.","DOI":"10.1145\/3627673.3679577"}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Dublin Ireland","acronym":"MM '25"},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3755334","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:05:16Z","timestamp":1765339516000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3755334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":37,"alternative-id":["10.1145\/3746027.3755334","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3755334","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}