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 311–320 of 982 papers

TitleStatusHype
Graph-Level Embedding for Time-Evolving Graphs—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Decoupling feature propagation from the design of graph auto-encoders—0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Debiasing Graph Representation Learning based on Information Bottleneck—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Data Considerations in Graph Representation Learning for Supply Chain Networks—0
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Benchmark Results

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