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Found 5 papers, 1 papers with code
Causal Lifting and Link Prediction
Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph.
Reconstruction for Powerful Graph Representations
Empirically, we show how reconstruction can boost GNN's expressive power -- while maintaining its invariance to permutations of the vertices -- by solving seven graph property tasks not solvable by the original GNN.
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.
Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.
Graph Pattern Mining and Learning through User-defined Relations (Extended Version)
In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation.
