SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 221–230 of 982 papers

TitleStatusHype
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Negative Sampling in Knowledge Graph Representation Learning: A Review—0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Representation learning in multiplex graphs: Where and how to fuse information?Code0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified