gemslab Goto Github PK
Name: GEMS Lab: Graph Exploration & Mining at Scale, University of Michigan
Type: Organization
Bio: Code repository for work by the GEMS Lab: https://gemslab.github.io/research/
Location: Ann Arbor, MI
Name: GEMS Lab: Graph Exploration & Mining at Scale, University of Michigan
Type: Organization
Bio: Code repository for work by the GEMS Lab: https://gemslab.github.io/research/
Location: Ann Arbor, MI
Network alignment using proximity-preserving node embedding and subspace alignment
Node embedding for directed, weighted networks
Personalized knowledge graph summarization based on historical queries
GNN with summarization/grouping layer for speed and interpretability
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
Hashing-based network alignment based on structural features
Hashing-based network discovery from time series
How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)
Knowledge Graph summarization for anomaly/error detection & completion (WebConf '20)
Select a set of pairs to check in link prediction from the vast, sparse space of possible pairs
Slides and code for the Morgan Claypool book on "Individual and Collective Graph Mining: Principles, Algorithms and Applications"
Framework for latent network summarization: bridging network embedding and summarization
Network datasets of post-PhD career transitions and trajectories in computing research
Compact time- and attribute-aware node representations
Measuring the persistence of activity snippets in evolving networks (KDD '20)
Representation learning-based graph alignment based on implicit matrix factorization and structural embeddings
Fast embedding-based graph classification with connections to kernels
The SEMB library is an easy-to-use tool for getting and evaluating structural node embeddings in graphs.
Summarization of static graphs using the Minimum Description Length principle
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