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 861–870 of 982 papers

TitleStatusHype
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Graph-based prediction of Protein-protein interactions with attributed signed graph embeddingCode1
Second-Order Pooling for Graph Neural NetworksCode1
Deep Representation Learning For Multimodal Brain Networks—0
Recent Advances in Network-based Methods for Disease Gene PredictionCode0
Towards Deeper Graph Neural NetworksCode1
Are Hyperbolic Representations in Graphs Created Equal?—0
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers—0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
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Benchmark Results

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