Distributed Training of Graph Convolutional Networks using Subgraph Approximation.
Alexandra AngerdKeshav BalasubramanianMurali AnnavaramPublished in: CoRR (2020)
Keyphrases
- graph mining
- edge weights
- graph databases
- graph properties
- computer networks
- graph mining algorithms
- peer to peer networks
- labeled graphs
- frequent subgraph mining
- graph data
- weighted graph
- heterogeneous networks
- distributed systems
- graph structures
- social networks
- subgraph isomorphism
- graph model
- peer to peer
- bipartite graph
- community discovery
- subgraph matching
- network nodes
- approximation algorithms
- maximum matching
- average degree
- maximum weight
- graph classification
- directed graph
- maximum clique
- small world
- structured data
- multi agent
- graph layout
- np hard
- neural network
- betweenness centrality
- graph structure
- network analysis
- deep learning
- mobile agents
- graph theoretic
- directed acyclic graph
- fully connected
- stable set
- communication cost
- link analysis
- subgraph mining
- graph matching
- spanning tree
- graph partitioning
- training set
- overlapping communities
- biological networks
- sparse coding
- recurrent networks
- supervised learning
- restricted boltzmann machine