Sgcn: A graph sparsifier based on graph convolutional networks
Graphs are ubiquitous across the globe and within science and engineering. With graphs
growing in size, node classification on large graphs can be space and time consuming, even
with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some
questions are raised, particularly, whether one can keep only some of the edges of a graph
while maintaining prediction performance for node classification, or train classifiers on
specific subgraphs instead of a whole graph with limited performance loss in node …
growing in size, node classification on large graphs can be space and time consuming, even
with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some
questions are raised, particularly, whether one can keep only some of the edges of a graph
while maintaining prediction performance for node classification, or train classifiers on
specific subgraphs instead of a whole graph with limited performance loss in node …
[PDF][PDF] SGCN: A Graph Sparsifier Based on Graph Convolutional Networks
R Zafarani - academia.edu
Graphs are ubiquitous across the globe and within science and engineering. With graphs
growing in size, node classification on large graphs can be space and time consuming, even
with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some
questions are raised, particularly, whether one can keep only some of the edges of a graph
while maintaining prediction performance for node classification, or train classifiers on
specific subgraphs instead of a whole graph with limited performance loss in node …
growing in size, node classification on large graphs can be space and time consuming, even
with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some
questions are raised, particularly, whether one can keep only some of the edges of a graph
while maintaining prediction performance for node classification, or train classifiers on
specific subgraphs instead of a whole graph with limited performance loss in node …
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