Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs.
Saurav ManchandaDa ZhengGeorge KarypisPublished in: CoRR (2021)
Keyphrases
- graph structures
- average degree
- real world graphs
- highly connected
- small world
- edge weights
- graph theory
- graph layout
- graph structure
- graph representation
- graph mining
- directed graph
- social graphs
- community discovery
- weighted graph
- dynamic networks
- random graphs
- graph matching
- graph clustering
- deep learning
- graph construction
- degree distribution
- graph theoretic
- heterogeneous networks
- graph properties
- graph databases
- protein interaction networks
- labeled graphs
- betweenness centrality
- complex networks
- adjacency matrix
- social networks
- fully connected
- graph theoretical
- graph partitioning
- undirected graph
- graph classification
- graph search
- real world social networks
- structural pattern recognition
- subgraph isomorphism
- graph mining algorithms
- graph model
- community detection
- massive graphs
- graph isomorphism
- series parallel
- network structure
- spanning tree
- network analysis
- real world networks
- graph data
- small world networks
- attributed graphs
- bipartite graph
- graph representations
- minimum spanning tree
- scale free
- biological networks
- topological information
- densely connected
- finding the shortest path
- data model
- neighborhood graph
- random walk
- connected components
- maximum clique
- graphical structure
- graph patterns
- query graph
- planar graphs
- dynamic graph
- reachability queries
- shortest path
- clustering coefficient
- graphical models
- directed edges
- functional modules
- community structure
- maximal cliques