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

Showing 331340 of 982 papers

TitleStatusHype
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Connector 0.5: A unified framework for graph representation learningCode0
From ChebNet to ChebGibbsNetCode0
Heterogeneous Deep Graph InfomaxCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Frameless Graph Knowledge DistillationCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
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Benchmark Results

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