TransGCN: Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction.
Ling CaiBo YanGengchen MaiKrzysztof JanowiczRui ZhuPublished in: K-CAP (2019)
Keyphrases
- link prediction
- link formation
- random walk
- network analysis
- bipartite graph
- graph mining
- social networks
- network structure
- biological networks
- social network analysis
- complex networks
- community detection
- missing links
- heterogeneous networks
- bipartite networks
- social and information networks
- real world networks
- social ties
- community discovery
- graph kernels
- topological features
- graph theory
- multi relational
- factor graph model
- small world
- proximity measures
- heterogeneous information networks
- link structure
- information networks
- protein interaction networks
- graph model
- markov chain
- clustering coefficient
- entity resolution
- functional modules
- directed graph
- location prediction
- graph data
- structured data
- decision trees
- collaborative filtering
- supervised learning
- degree distribution
- centrality measures
- scale free
- social interaction
- online social networks