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• W2995763485 abstract "Graph-structured data is prevalent in many domains. Despite the widely celebrated success of deep neural networks, their power in graph-structured data is yet to be fully explored. We propose a novel network architecture that incorporates advanced graph structural features. In particular, we leverage discrete graph curvature, which measures how the neighborhoods of a pair of nodes are structurally related. The curvature of an edge (x, y) defines the distance taken to travel from neighbors of x to neighbors of y, compared with the length of edge (x, y). It is a much more descriptive feature compared to previously used features that only focus on node specific attributes or limited topological information such as degree. Our curvature graph convolution network outperforms state-of-the-art on various synthetic and real-world graphs, especially the larger and denser ones." @default.
• W2995763485 created "2019-12-26" @default.
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• W2995763485 date "2020-04-30" @default.
• W2995763485 modified "2023-09-24" @default.
• W2995763485 title "Curvature Graph Network" @default.
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• W2995763485 hasPublicationYear "2020" @default.
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