Post #59

Fresh picks from ArXiv More ICML and KDD submissions and large body on mathematical graph theory 📖 ICML Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization Neural Networks on Random Graphs Embedding Graph Auto-Encoder with Joint Clustering via Adjacency Sharing Adaptive Graph Auto-Encoder for General Data Clustering Computationally Tractable Riemannian Manifolds for Graph Embeddings Set2Graph: Learning Graphs From Sets Node Masking: Making Graph Neural Networks Generalize and Scale Better Deep Graph Mapper: Seeing Graphs through the Neural Lens Learning Dynamic Knowledge Graphs to Generalize on Text-Based Games by Microsoft and group of William L. Hamilton Learning to Simulate Complex Physics with Graph Networks by Deepmind + group of Jure Leskovec KDD Self-Enhanced GNN: Improving Graph Neural Networks UsingModel Outputs Graph4Code: A Machine Interpretable Knowledge Graph for Code Localized Flow-Based Clustering in Hypergraphs by group of Jon Kleinberg WWW Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction Graph Theory Building large k-cores from sparse graphs Distributed graph problems through an automata-theoretic lens Computing the k Densest Subgraphs of a Graph Seeing Far vs. Seeing Wide: Volume Complexity of Local Graph Problems Planar graphs have bounded queue-number Review Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations