69 citations · 87 across the 8 of their papers we have counts for
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cs.LG2021★ 1 cited
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning
Jun Hu, Shengsheng Qian, Quan Fang +1
Graph Neural Networks (GNNs) have achieved great success in processing graph data by extracting and propagating structure-aware features. Existing GNN research designs various prop…
cs.LG2021★ 9 cited
Efficient Graph Deep Learning in TensorFlow with tf_geometric
Jun Hu, Shengsheng Qian, Quan Fang +4
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries f…
cs.LG2018★ 1 cited
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding
Lei Sang, Min Xu, Shengsheng Qian +1
Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach…