Using Causality-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs.
Franziska HeegIngo ScholtesPublished in: CoRR (2023)
Keyphrases
- dynamic graph
- neural network
- dynamic networks
- graph representation
- graph theory
- temporal sequences
- graph matching
- pattern recognition
- graph theoretic
- directed graph
- weighted graph
- graph model
- graph structure
- graph properties
- graph construction
- graph clustering
- graph classification
- graph partitioning
- graph databases
- labeled graphs
- graph data
- graph structures
- structural pattern recognition
- graph isomorphism
- graph theoretical
- artificial neural networks
- adjacency matrix
- graph search
- bipartite graph
- graph mining
- reachability queries
- dense subgraphs
- minimum spanning tree
- spatio temporal
- planar graphs
- subgraph isomorphism
- graph transformation
- series parallel
- maximum clique
- connected dominating set
- maximal cliques
- community discovery
- graph embedding
- random graphs
- spanning tree
- temporal reasoning
- temporal ordering
- edge weights
- undirected graph
- polynomial time complexity
- temporal data
- complex networks
- structured data
- finding the shortest path
- random walk