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Found 6 papers, 3 papers with code
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy
Thus, without sacrificing accuracy, graph sparsification, or model compression becomes a viable approach for graph learning tasks.
MarkovGNN: Graph Neural Networks on Markov Diffusion
Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities.
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods
The learned embeddings have been successfully applied to perform various prediction tasks, such as link prediction, node classification, clustering, and visualization.
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks
We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparse-dense matrix multiplication under a single operation called FusedMM.
Force2Vec: Parallel force-directed graph embedding
A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph.
Training Sensitivity in Graph Isomorphism Network
Graph neural network (GNN) is a popular tool to learn the lower-dimensional representation of a graph.
