Post #331

Fresh picks from ArXiv Today at ArXiv many interesting papers. Debunking performance of GNN with MLP models, a new python library for graph reconstruction and distances, scalable and reliable GNNs, and more papers from NeurIPS 2020. If I forgot to mention your paper, please shoot me a message and I will update the post. Conferences - Handling Missing Data with Graph Representation Learning with Jure Leskovec, NeurIPS 2020 - Scalable Graph Neural Networks via Bidirectional Propagation NeurIPS 2020 - Reliable Graph Neural Networks via Robust Aggregation with Stephan Günnemann, NeurIPS 2020 - Strongly Incremental Constituency Parsing with Graph Neural Networks NeurIPS 2020 - Graph Contrastive Learning with Augmentations NeurIPS 2020 - Graph Neural Network for Metal Organic Framework Potential Energy Approximation Workshop NeurIPS 2020 - Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management ACL 2021 - Be More with Less: Hypergraph Attention Networks for Inductive Text Classification EMNLP 2020 - Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks EMNLP 2020 - AutoAudit: Mining Accounting and Time-Evolving Graphs with Christos Faloutsos, Big Data 2020 Graphs - Log(Graph): A Near-Optimal High-Performance Graph Representation - netrd: A library for network reconstruction and graph distances - Revisiting Graph Neural Networks for Link Prediction - Graph Contrastive Learning with Adaptive Augmentation - On Graph Neural Networks versus Graph-Augmented MLPs with Joan Bruna - Combining Label Propagation and Simple Models Out-performs Graph Neural Networks Survey - Domain-specific Knowledge Graphs: A survey