Curvature Graph Network
Curvature Graph Network
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发表时间：
2020-04
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通讯作者：
Ze Ye;Kin Sum Liu;Tengfei Ma;Jie Gao;Chao Chen
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作者：
Ze Ye;Kin Sum Liu;Tengfei Ma;Jie Gao;Chao Chen
图结构数据在许多领域都很普遍。尽管深度神经网络取得了广泛的成功，但它们在图结构数据中的力量尚未得到充分探索。我们提出了一种新颖的网络架构，它结合了先进的图结构特征。特别是，我们利用离散图曲率，它测量一对节点的邻域在结构上如何相关。边 (x, y) 的曲率定义了与边 (x, y) 的长度相比，从 x 的邻居到 y 的邻居行进的距离。与以前使用的仅关注节点特定属性或有限的拓扑信息（例如度数）的特征相比，它是一个更具描述性的特征。我们的曲率图卷积网络在各种合成图和现实世界图上的性能优于最先进的图，尤其是更大和更密集的图。
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.