# Projects

https://www.mmds.org/projects.html

[![By Jure Leskovec](https://www.mmds.org/images/by_jure.gif)](http://cs.stanford.edu/~jure/)![](https://www.mmds.org/images/empty_logo.png)[![Stanford University](https://www.mmds.org/images/stanford.png)](http://www.stanford.edu/)
[![SNAP logo](https://www.mmds.org/images/snap_logo.png)](https://www.mmds.org/index.html)
  * [SNAP for C++](https://www.mmds.org/snap/index.html)
    * [SNAP C++ Main Page](https://www.mmds.org/snap/index.html)
    * [SNAP C++ Download](https://www.mmds.org/snap/download.html)
    * [SNAP C++ Documentation](https://www.mmds.org/snap/doc.html)
  * [SNAP for Python](https://www.mmds.org/snappy/index.html)
    * [Snap.py Python Main Page](https://www.mmds.org/snappy/index.html)
    * [Snap.py Python Download](https://www.mmds.org/snappy/index.html#download)
    * [Snap.py Python Documentation](https://www.mmds.org/snappy/index.html#docs)
  * [SNAP Datasets](https://www.mmds.org/data/index.html)
    * [Large networks](https://www.mmds.org/data/index.html)
    * [Web datasets](https://www.mmds.org/data/other.html)
    * [Other resources](https://www.mmds.org/data/links.html)
  * [BIOSNAP Datasets](https://www.mmds.org/biodata/index.html)
  * [What's new](https://www.mmds.org/news.html)
  * [People](https://www.mmds.org/people.html)
  * [Papers](https://www.mmds.org/papers.html)
  * [Projects](https://www.mmds.org/projects.html)
    * [Activity Inequality](http://snap.stanford.edu/activity-inequality)
    * [AGM](http://snap.stanford.edu/agm)
    * [BetaE](http://snap.stanford.edu/betae)
    * [CAW](http://snap.stanford.edu/caw)
    * [COMET](http://snap.stanford.edu/comet)
    * [ConE](http://snap.stanford.edu/cone)
    * [Conflict](http://snap.stanford.edu/conflict)
    * [ConNIe](http://snap.stanford.edu/connie)
    * [Counseling](http://snap.stanford.edu/counseling)
    * [CRank](http://snap.stanford.edu/crank)
    * [Distance-encoding](http://snap.stanford.edu/distance-encoding)
    * [Decagon](http://snap.stanford.edu/decagon)
    * [F-FADE](http://snap.stanford.edu/f-fade)
    * [GIB](http://snap.stanford.edu/gib)
    * [GNN-Design](http://snap.stanford.edu/gnn-design)
    * [GNN-Explainer](http://snap.stanford.edu/gnnexplainer)
    * [GNN-pretrain](http://snap.stanford.edu/gnn-pretrain)
    * [GRAPE](http://snap.stanford.edu/grape)
    * [GraphSAGE](http://snap.stanford.edu/graphsage)
    * [GraphWave](http://snap.stanford.edu/graphwave)
    * [G2SAT](http://snap.stanford.edu/g2sat)
    * [HGCN](http://snap.stanford.edu/hgcn)
    * [Higher-order](http://snap.stanford.edu/higher-order)
    * [ID-GNN](http://snap.stanford.edu/idgnn)
    * [Disinformation](http://snap.stanford.edu/hoax)
    * [InfoPath](http://snap.stanford.edu/infopath)
    * [JODIE](http://snap.stanford.edu/jodie)
    * [LIM](http://snap.stanford.edu/lim)
    * [MAPPR](http://snap.stanford.edu/mappr)
    * [MAMBO](http://snap.stanford.edu/mambo)
    * [MARS](http://snap.stanford.edu/mars)
    * [Memetracker](http://snap.stanford.edu/memetracker)
    * [NCP](http://snap.stanford.edu/ncp)
    * [NE](http://snap.stanford.edu/ne)
    * [NETINF](http://snap.stanford.edu/netinf)
    * [NIFTY](http://snap.stanford.edu/nifty/)
    * [node2vec](http://snap.stanford.edu/node2vec)
    * [Ocean](http://snap.stanford.edu/ocean)
    * [OhmNet](http://snap.stanford.edu/ohmnet)
    * [ORCA](http://snap.stanford.edu/orca)
    * [NeuroMatch](http://snap.stanford.edu/subgraph-matching)
    * [Pathways](http://snap.stanford.edu/pathways)
    * [P-GNN](http://snap.stanford.edu/pgnn)
    * [Query2box](http://snap.stanford.edu/query2box)
    * [QUOTUS](http://snap.stanford.edu/quotus)
    * [Ringo](http://snap.stanford.edu/ringo)
    * [SEISMIC](http://snap.stanford.edu/seismic)
    * [SNAP](http://snap.stanford.edu/snap)
    * [Snap.py](http://snap.stanford.edu/snappy)
    * [SnapVX](http://snap.stanford.edu/snapvx)
    * [SpatialWhisperer](http://snap.stanford.edu/spatialwhisperer)
    * [SPMiner](http://snap.stanford.edu/frequent-subgraph-mining)
    * [STELLAR](http://snap.stanford.edu/stellar)
    * [Temporal Motifs](http://snap.stanford.edu/temporal-motifs)
    * [TICC](http://snap.stanford.edu/ticc)
    * [TIPAS](http://snap.stanford.edu/tipas)
    * [Tree of Life](http://snap.stanford.edu/tree-of-life)
    * [TVGL](http://snap.stanford.edu/tvgl)
  * [Citing SNAP](https://www.mmds.org/citing.html)
  * [Links](https://www.mmds.org/links.html)
  * [About](https://www.mmds.org/about.html)
  * [Contact us](https://www.mmds.org/contact.html)


Open positions
We are inviting applications for postdoctoral positions in **[Foundation Models for Biomedicine](http://snap.stanford.edu/apply/index-postdoc.php)**. We have open positions for **[undergraduate and graduate](http://snap.stanford.edu/apply/index.php)** research assistants. The application form and project descriptions can be found **[here](https://snap.stanford.edu/apply/index.php)**. 
# Projects
  * [AGM](http://snap.stanford.edu/agm) : Model-based Approach to Detecting Densely Overlapping Communities in Networks
  * [BetaE](http://snap.stanford.edu/betae) : Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
  * [BiDyn](http://snap.stanford.edu/bidyn) : BiDyn: Bipartite Dynamic Representations for Abuse Detection
  * [COMET](http://snap.stanford.edu/comet) : Concept Learners for Generalizable Few-Shot Learning
  * [ConNIe](http://snap.stanford.edu/connie) : Inferring Networks of Diffusion and Influence
  * [ConE](http://snap.stanford.edu/cone) : Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
  * [Counseling](http://snap.stanford.edu/counseling) : Counseling Conversation Analysis
  * [CRank](http://snap.stanford.edu/crank) : Prioritizing Network Communities
  * [Distance-encoding](http://snap.stanford.edu/distance-encoding) : Design Provably More Powerful GNNs for Structural Representation Learning
  * [Decagon](http://snap.stanford.edu/decagon) : Graph Neural Network for Multirelational Link Prediction
  * [F-FADE](http://snap.stanford.edu/f-fade) : Frequency Factorization for Anomaly Detection in Edge Streams
  * [GIB](http://snap.stanford.edu/gib) : Graph Information Bottleneck
  * [GNN-Design](http://snap.stanford.edu/gnn-design) : Design Space for Graph Neural Networks
  * [GNN-Explainer](http://snap.stanford.edu/gnnexplainer) : Generating Explanations for Graph Neural Networks
  * [GNN-pretrain](http://snap.stanford.edu/gnn-pretrain) : Strategies for Pre-training Graph Neural Networks
  * [GRAPE](http://snap.stanford.edu/grape) : Handling Missing Data with Graph Representation Learning
  * [GraphWave](http://snap.stanford.edu/graphwave) : Learning Structural Node Embeddings
  * [G2SAT](http://snap.stanford.edu/g2sat) : Learning to Generate SAT Formulas
  * [HGCN](http://snap.stanford.edu/hgcn) : Hyperbolic Graph Convolutional Neural Networks
  * [Higher-order](http://snap.stanford.edu/higher-order) : Higher-order organization of complex networks
  * [ID-GNN](http://snap.stanford.edu/idgnn) : Identity-aware Graph Neural Networks
  * [InfoPath](http://snap.stanford.edu/infopath) : Structure and Dynamics of Information Pathways in On-line Media
  * [JODIE](http://snap.stanford.edu/jodie) : Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
  * [LIM](http://snap.stanford.edu/lim) : Linear Influence Model
  * [NCP](http://snap.stanford.edu/ncp) : Network Community Profile
  * [MAMBO](http://snap.stanford.edu/mambo) : Construction and Representation of Multimodal Biomedical Networks
  * [MAPPR](http://snap.stanford.edu/mappr) : Local Higher-ordering Clustering with MAPPR method
  * [MARS](http://snap.stanford.edu/mars) : Discovering Novel Cell Types across Heterogeneous Single-cell Experiments
  * [NE](http://snap.stanford.edu/ne) : Network Enhancement
  * [NETINF](http://snap.stanford.edu/netinf) : Inferring Networks of Diffusion and Influence
  * [NeuroMatch](http://snap.stanford.edu/subgraph-matching) : Neural Subgraph Matching
  * [NIFTY](http://snap.stanford.edu/nifty/) : A System for Large Scale Information Flow Tracking and Clustering
  * [node2vec](http://snap.stanford.edu/node2vec) : Scalable Feature Learning for Networks
  * [QUOTUS](http://snap.stanford.edu/quotus) : The Structure of Political Media Coverage as Revealed by Quoting Patterns
  * [Query2box](http://snap.stanford.edu/query2box) : Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings
  * [QAGNN](http://snap.stanford.edu/qagnn) : Reasoning with Language Models and Knowledge Graphs for Question Answering
  * [Ocean](http://snap.stanford.edu/ocean) : Online Task Inference for Compositional Tasks with Context Adaptation
  * [OhmNet](http://snap.stanford.edu/ohmnet) : Feature Learning in Multi-Layer Tissue Networks
  * [ORCA](http://snap.stanford.edu/orca) : Open-World Semi-Supervised Learning
  * [Pathways](http://snap.stanford.edu/pathways) : Disease Pathways in the Human Interactome
  * [Persuasion](http://snap.stanford.edu/persuasion) : M2P2: Multimodal Persuasive Prediction using Adaptive Fusion
  * [P-GNN](http://snap.stanford.edu/pgnn) : Position-aware Graph Neural Networks
  * [Ringo](http://snap.stanford.edu/ringo) : In-Memory Graph Exploration System
  * [SEISMIC](http://snap.stanford.edu/seismic) : A Self-Exciting Point Process Model for Predicting Tweet Popularity
  * [SNAP](http://snap.stanford.edu/snap) : Stanford Network Analysis Platform
  * [Snap.py](http://snap.stanford.edu/snappy) : SNAP for Python
  * [SnapVX](http://snap.stanford.edu/snapvx) : Network-Based Optimization Solver
  * [SpatialWhisperer](http://snap.stanford.edu/spatialwhisperer) : Transitive Representation Learning Enhances Histopathology Annotation
  * [SPMiner](http://snap.stanford.edu/frequent-subgraph-mining) : SPMiner: Frequent Subgraph Mining by Walking in Order Embedding Space
  * [STELLAR](http://snap.stanford.edu/stellar) : Annotation of Spatially Resolved Single-cell Data
  * [Tree of Life](http://snap.stanford.edu/tree-of-life) : Evolution of protein interactomes across the tree of life


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