38 citations · 163 across the 25 of their papers we have counts for
6 papers · 1 filter
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Ningyi Liao, Dingheng Mo, Siqiang Luo +2
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in sol…
Proteus: A Self-Designing Range Filter
Eric R. Knorr, Baptiste Lemaire, Andrew Lim +4
We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given…
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng +4
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily.…
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…
DMCS : Density Modularity based Community Search
Junghoon Kim, Siqiang Luo, Gao Cong +1
Community Search, or finding a connected subgraph (known as a community) containing the given query nodes in a social network, is a fundamental problem. Most of the existing commun…
A Survey on Machine Learning Solutions for Graph Pattern Extraction
Kai Siong Yow, Ningyi Liao, Siqiang Luo +3
A subgraph is constructed by using a subset of vertices and edges of a given graph. There exist many graph properties that are hereditary for subgraphs. Hence, researchers from dif…