7 citations · 20 across the 9 of their papers we have counts for
5 papers · 1 filter
Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
Zulun Zhu, Siqiang Luo
Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas ma…
Revisiting Graph Autoencoders as Implicit Contrastive Learners
Jintang Li, Ruofan Wu, Yuchang Zhu +3
Graph autoencoders (GAEs) and graph contrastive learning (GCL) are two major paradigms for self-supervised representation learning on graphs, yet they are often studied in isolatio…
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness
Ningyi Liao, Haoyu Liu, Zulun Zhu +2
With recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their ability to retrieve graph signals in the spectral dom…
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…
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…