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However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology structures in a payment network may reveal sellers\u2019 business roles (e.g., supplier or retailer). To model both connectivity and local topology structure for better node classification performance, we present DP-GCN, a dual-path graph convolution network. DP-GCN consists of three main modules: (i) a C-GCN module to capture the connectivity relationships between nodes, (ii) a T-GCN module to capture the topology structure similarity among nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines to demonstrate its effectiveness. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, a leading payment service provider, for risky seller detection. Experimental results show DP-GCN\u2019s effectiveness and practicability in large-scale settings. PayPal\u2019s internal testing also shows DP-GCN\u2019s effectiveness in defending against real risks from transaction networks.<\/jats:p>","DOI":"10.1145\/3649460","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T12:52:27Z","timestamp":1709124747000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5902-4045","authenticated-orcid":false,"given":"Zhe","family":"Chen","sequence":"first","affiliation":[{"name":"Innovation Lab, PayPal, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0764-4258","authenticated-orcid":false,"given":"Aixin","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3006475"},{"key":"e_1_3_2_3_2","article-title":"Time and space complexity of graph convolutional networks","volume":"31","author":"Blakely Derrick","year":"2021","unstructured":"Derrick Blakely, Jack Lanchantin, and Yanjun Qi. 2021. 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