112 citations · 295 across the 13 of their papers we have counts for
5 papers · 1 filter
K-Core Decomposition on Super Large Graphs with Limited Resources
Shicheng Gao, Jie Xu, Xiaosen Li +5
K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the sc…
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
Xupeng Miao, Hailin Zhang, Yining Shi +4
Embedding models have been an effective learning paradigm for high-dimensional data. However, one open issue of embedding models is that their representations (latent factors) ofte…
Node Dependent Local Smoothing for Scalable Graph Learning
Wentao Zhang, Mingyu Yang, Zeang Sheng +5
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs). Concretely, they show feature smoothing combined with simple linear regression…
Graph Attention MLP with Reliable Label Utilization
Wentao Zhang, Ziqi Yin, Zeang Sheng +5
Graph neural networks (GNNs) have recently achieved state-of-the-art performance in many graph-based applications. Despite the high expressive power, they typically need to perform…
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
Wentao Zhang, Yu Shen, Zheyu Lin +6
In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-b…