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May 28, 2023 · We evaluate GTEA on a payment dataset, denoted as Mobile-Pay, which is provided by a major mobile payment provider. We additionally assess GTEA on two Ethereum phishing datasets Footnote 2 with different scales, denoted as Phish-L(arge) and Phish-S(mall).
Sep 11, 2020 · In this paper, we propose the Graph Temporal Edge Aggregation (GTEA) framework for inductive learning on Temporal Interaction Graphs (TIGs). Different from previous works, GTEA models the temporal dynamics of interaction sequences in the continuous-time space and simultaneously takes advantage of both rich node and edge/ interaction attributes in the graph. Concretely, we integrate a sequence ...
in a network, GTEA can learn fine-grained representations for a temporal interaction graph to enable or facilitate other downstream data analytic tasks. Experimental results show that GTEA outperforms state-of-the-art schemes including GraphSAGE, APPNP, and TGAT by delivering higher ac-curacy (100.00%, 98.51%, 98.05% ,79.90%) and macro-F1
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