Binarized Graph Neural Network

19 Apr 2020Hanchen WangDefu LianYing ZhangLu QinXiangjian HeYiguang LinXuemin Lin

Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based graph embedding approaches which may limit the efficiency and scalability of these models... (read more)

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