Projects

• AGM : Model-based Approach to Detecting Densely Overlapping Communities in Networks
• BetaE : Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
• BiDyn : BiDyn: Bipartite Dynamic Representations for Abuse Detection
• COMET : Concept Learners for Generalizable Few-Shot Learning
• ConNIe : Inferring Networks of Diffusion and Influence
• ConE : Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
• Counseling : Counseling Conversation Analysis
• CRank : Prioritizing Network Communities
• Distance-encoding : Design Provably More Powerful GNNs for Structural Representation Learning
• Decagon : Graph Neural Network for Multirelational Link Prediction
• F-FADE : Frequency Factorization for Anomaly Detection in Edge Streams
• GIB : Graph Information Bottleneck
• GNN-Design : Design Space for Graph Neural Networks
• GNN-Explainer : Generating Explanations for Graph Neural Networks
• GNN-pretrain : Strategies for Pre-training Graph Neural Networks
• GRAPE : Handling Missing Data with Graph Representation Learning
• GraphWave : Learning Structural Node Embeddings
• G2SAT : Learning to Generate SAT Formulas
• HGCN : Hyperbolic Graph Convolutional Neural Networks
• Higher-order : Higher-order organization of complex networks
• ID-GNN : Identity-aware Graph Neural Networks
• InfoPath : Structure and Dynamics of Information Pathways in On-line Media
• JODIE : Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
• LIM : Linear Influence Model
• Local AI : Local AI
• NCP : Network Community Profile
• MAMBO : Construction and Representation of Multimodal Biomedical Networks
• MAPPR : Local Higher-ordering Clustering with MAPPR method
• MARS : Discovering Novel Cell Types across Heterogeneous Single-cell Experiments
• NE : Network Enhancement
• NETINF : Inferring Networks of Diffusion and Influence
• NeuroMatch : Neural Subgraph Matching
• NIFTY : A System for Large Scale Information Flow Tracking and Clustering
• node2vec : Scalable Feature Learning for Networks
• Ocean : Online Task Inference for Compositional Tasks with Context Adaptation
• OhmNet : Feature Learning in Multi-Layer Tissue Networks
• ORCA : Open-World Semi-Supervised Learning
• Pathways : Disease Pathways in the Human Interactome
• Persuasion : M2P2: Multimodal Persuasive Prediction using Adaptive Fusion
• P-GNN : Position-aware Graph Neural Networks
• QAGNN : Reasoning with Language Models and Knowledge Graphs for Question Answering
• Query2box : Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings
• QUOTUS : The Structure of Political Media Coverage as Revealed by Quoting Patterns
• Ringo : In-Memory Graph Exploration System
• SEISMIC : A Self-Exciting Point Process Model for Predicting Tweet Popularity
• SNAP : Stanford Network Analysis Platform
• Snap.py : SNAP for Python
• SnapVX : Network-Based Optimization Solver
• SpatialWhisperer : Transitive Representation Learning Enhances Histopathology Annotation
• SPMiner : SPMiner: Frequent Subgraph Mining by Walking in Order Embedding Space
• STELLAR : Annotation of Spatially Resolved Single-cell Data
• Tree of Life : Evolution of protein interactomes across the tree of life