KDD2020.md

KDD 2020

All accept papers list：https://www.kdd.org/kdd2020/accepted-papers

• Research Track
  • Graph Convolutional
  • Graph Generation
  • Graph Attention
  • Graph Encoder
  • Dynamic Graphs
  • Heterogeneous Graph
  • Graph Representation Pre-Training
  • Graph Neural Networks
  • Knowledge Graph
  • Hypergraph
• More
  • Molecular
  • Time Series
  • Graph Representation Learning
  • Graph Meta-learning
  • Recommendation
  • Other
• Applied Data Science Track
  • Baidu
  • DiDi
  • Alibaba
  • Adobe
  • Google
  • Other

Research Track

Graph Convolutional

• A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
• Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data
• Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations
• Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

Graph Generation

• A Data Driven Graph Generative Model for Temporal Interaction Networks
• Node-Edge Co-disentangled Representation Learning for Attributed Graph Generation

Graph Attention

• DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation
• Graph Attention Networks over Edge Content-Based Channels

Graph Encoder

• Adaptive Graph Encoder for Attributed Graph Embedding

Dynamic Graphs

• Laplacian Change Point Detection for Dynamic Graphs

Heterogeneous Graph

• An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
• HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness

Graph Representation Pre-Training

• GPT-GNN: Generative Pre-Training of Graph Neural Networks
• Graph Contrastive Coding for Structural Graph Representation Pre-Training

Graph Neural Networks

• TinyGNN: Learning Efficient Graph Neural Networks
• Towards Deeper Graph Neural Networks
• Redundancy-Free Computation for Graph Neural Networks
• Graph Structure Learning for Robust Graph Neural Networks
• Graph Structural-topic Neural Network
• Learning Effective Road Network Representation with Hierarchical Graph Neural Networks
• Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
• PolicyGNN: Aggregation Optimization for Graph Neural Networks
• Residual Correlation in Graph Neural Network Regression
• XGNN: Towards Model-Level Explanations of Graph Neural Networks

Knowledge Graph

• Dynamic Knowledge Graph based Multi-Event Forecasting
• Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams
• MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
• REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs

Hypergraph

• Dual Channel Hypergraph Collaborative Filtering
• Hypergraph Clustering Based on PageRank
• Minimizing Localized Ratio Cut Objectives in Hypergraphs
• Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs
• Structural Patterns and Generative Models of Real-world Hypergraphs

More

Molecular

• ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
• MoFlow: An Invertible Flow Model for Generating Molecular Graphs

Time Series

• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Graph Representation Learning

• Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning
• Understanding Negative Sampling in Graph Representation Learning

Graph Meta-learning

• Task-Adaptive Graph Meta-learning

Recommendation

• Handling Information Loss of Graph Neural Networks for Session-based Recommendation
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
• Interactive Path Reasoning on Graph for Conversational Recommendation

Other

• Efficient Algorithm for the b-Matching Graph
• GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases
• HOPS: Probabilistic Subtree Mining for Small and Large Graphs
• How to count triangles, without seeing the whole graph
• In and Out: Optimizing Overall Interaction in Probabilistic Graphs under Clustering Constraints
• Incremental Lossless Graph Summarization
• InFoRM: Individual Fairness on Graph Mining
• Learning Stable Graphs from Heterogeneous Confounded Environments
• Local Motif Clustering on Time-Evolving Graphs
• Neural Subgraph Isomorphism Counting
• Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism
• Prioritized Restreaming Algorithms for Balanced Graph Partitioning
• SSumM: Sparse Summarization of Massive Graphs
• Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation

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Applied Data Science Track

Baidu

• ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps

DiDi

• Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction
• Gemini: A novel and universal heterogeneous graph information fusing framework for online recommendations

Alibaba

• Balanced Order Batching with Task-Oriented Graph Clustering
• M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems
• Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
• A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce

Adobe

• Personalized Image Retrieval with Sparse Graph Representation Learning

Google

• Scaling Graph Neural Networks with Approximate PageRank
• Grale: Designing Networks for Graph Learning

Other

• Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
• Domain Specific Knowledge Graphs as a Service to the Public
• Explainable classification of brain networks via contrast subgraphs
• Hypergraph Convolutional Recurrent Neural Network