Exploiting Instance Graphs and Graph Neural Networks for Next Activity Prediction.
Andrea ChiorriniClaudia DiamantiniAlex MircoliDomenico PotenaPublished in: ICPM Workshops (2021)
Keyphrases
- neural network
- graph representation
- graph matching
- directed graph
- graph theory
- graph structure
- graph mining
- labeled graphs
- graph theoretic
- graph databases
- graph structures
- subgraph isomorphism
- graph construction
- weighted graph
- adjacency matrix
- pattern recognition
- graph model
- graph properties
- graph theoretical
- prediction accuracy
- graph search
- graph partitioning
- dynamic graph
- graph classification
- spanning tree
- graph data
- series parallel
- graph clustering
- bipartite graph
- graph isomorphism
- structural pattern recognition
- graph transformation
- random graphs
- prediction model
- minimum spanning tree
- multi layer perceptron
- directed acyclic
- graph kernels
- graph representations
- artificial neural networks
- evolving graphs
- graph drawing
- maximum common subgraph
- undirected graph
- small world
- edge weights
- structured data
- connected graphs
- graph layout
- inexact graph matching
- connected dominating set
- attributed graphs
- topological information
- maximal cliques
- neighborhood graph
- frequent subgraphs
- maximum clique
- random walk
- connected components
- web graph
- neural networks and support vector machines
- graph embedding
- polynomial time complexity
- real world graphs
- relational structures
- protein interaction
- maximum cardinality
- social graphs
- finding the shortest path
- planar graphs
- back propagation
- shortest path