GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs.
Linhao LuoYixiang FangMoli LuXin CaoXiaofeng ZhangWenjie ZhangPublished in: CoRR (2022)
Keyphrases
- graph structure
- graph representation
- directed graph
- graph theory
- weighted graph
- graph matching
- graph theoretic
- graph databases
- labeled graphs
- graph mining
- adjacency matrix
- random graphs
- graph model
- graph construction
- graph theoretical
- graph structures
- graph search
- subgraph isomorphism
- graph partitioning
- series parallel
- graph isomorphism
- graph properties
- graph kernels
- betweenness centrality
- graph clustering
- bipartite graph
- spanning tree
- graph representations
- graph transformation
- dynamic graph
- edge weights
- undirected graph
- structural pattern recognition
- graph classification
- graph connectivity
- directed acyclic
- graph data
- random walk
- attributed graphs
- maximum common subgraph
- topological information
- similarity scores
- probability measure
- graph layout
- finding the shortest path
- neural network
- polynomial time complexity
- graph drawing
- reachability queries
- information retrieval
- connected graphs
- inexact graph matching
- real world graphs
- maximum clique
- community discovery
- connected components
- graph patterns
- minimum spanning tree
- web graph
- similarity measure
- planar graphs
- complex networks
- dense subgraphs
- maximum cardinality
- connected dominating set
- structured data
- quasi cliques
- small world
- relational structures
- bounded treewidth
- social graphs
- vertex set
- adjacency graph