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 491–500 of 982 papers

TitleStatusHype
Instance-Aware Graph Prompt Learning—0
Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks—0
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Introducing Diminutive Causal Structure into Graph Representation Learning—0
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
JCapsR: 一种联合胶囊神经网络的藏语知识图谱表示学习模型(JCapsR: A Joint Capsule Neural Network for Tibetan Knowledge Graph Representation Learning)—0
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach—0
KAN KAN Buff Signed Graph Neural Networks?—0
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Benchmark Results

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