Post #168

Fresh picks from ArXiv This week is rich on explainable GNN literature, as well as papers on compression of graphs, combinatorial optimization and recommendation. GNN • Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time • Are Graph Convolutional Networks Fully Exploiting Graph Structure? • Accurately Solving Physical Systems with Graph Learning •Single-Layer Graph Convolutional Networks For Recommendation •XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks •XGNN: Towards Model-Level Explanations of Graph Neural Networks •Universal Graph Compression: Stochastic Block Models •Convergence and Stability of Graph Convolutional Networks on Large Random Graphs •Fairness-Aware Explainable Recommendation over Knowledge Graphs Graph Theory • Hierarchical hyperbolicity of graph products • Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs • The Weisfeiler-Leman dimension of chordal bipartite graphs without bipartite claw Conferences • Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction KDD 20 Surveys • Generate FAIR Literature Surveys with Scholarly Knowledge Graphs •A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions