2 citations · 7 across the 5 of their papers we have counts for
5 papers
CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction
Shuai Zhang, Zite Jiang, Haihang You
Graph neural network training is mainly categorized into mini-batch and full-batch training methods. The mini-batch training method samples subgraphs from the original graph in eac…
An Efficient Pruning Process with Locality Aware Exploration and Dynamic Graph Editing for Subgraph Matching
Zite Jiang, Boxiao Liu, Shuai Zhang +3
Subgraph matching is a NP-complete problem that extracts isomorphic embeddings of a query graph in a data graph . In this paper, we present a framework with three components…
Fast and Efficient Parallel Breadth-First Search with Power-law Graph Transformation
Zite Jiang, Tao Liu, Shuai Zhang +3
In the big data era, graph computing is widely used to exploit the hidden value in real-world graphs in various scenarios such as social networks, knowledge graphs, web searching,…
An Efficient and Balanced Graph Partition Algorithm for the Subgraph-Centric Programming Model on Large-scale Power-law Graphs
Shuai Zhang, Zite Jiang, Xingzhong Hou +3
The subgraph-centric programming model is a promising approach and has been applied in many state-of-the-art distributed graph computing frameworks. However, traditional graph part…
DRONE: a Distributed Subgraph-Centric Framework for Processing Large Scale Power-law Graphs
Xiaole Wen, Shuai Zhang, Haihang You
Nowadays, in the big data era, social networks, graph databases, knowledge graphs, electronic commerce etc. demand efficient and scalable capability to process an ever increasing v…