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DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network.
- Source :
- ACM Transactions on Knowledge Discovery from Data; Jul2024, Vol. 18 Issue 6, p1-20, 20p
- Publication Year :
- 2024
-
Abstract
- Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not the node's local topology structure. 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' 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's effectiveness and practicability in large-scale settings. PayPal's internal testing also shows DP-GCN's effectiveness in defending against real risks from transaction networks. [ABSTRACT FROM AUTHOR]
- Subjects :
- TOPOLOGY
CLASSIFICATION
PAYMENT
ROUTING algorithms
SUPPLIERS
Subjects
Details
- Language :
- English
- ISSN :
- 15564681
- Volume :
- 18
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- ACM Transactions on Knowledge Discovery from Data
- Publication Type :
- Academic Journal
- Accession number :
- 176927579
- Full Text :
- https://doi.org/10.1145/3649460