7 citations · 20 across the 9 of their papers we have counts for
Showing 2022Show all
3 papers · 1 filter
cs.NE2022★ 1 cited
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
Jintang Li, Zhouxin Yu, Zulun Zhu +6
Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However…
cs.LG2022★ 2 cited
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation
Yuansheng Wang, Wangbin Sun, Kun Xu +3
Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the major…
cs.LG2022★ 7 cited
Spiking Graph Convolutional Networks
Zulun Zhu, Jiaying Peng, Jintang Li +3
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemente…