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 691–700 of 982 papers

TitleStatusHype
Towards Interpretable Molecular Graph Representation Learning—0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
Transferable Graph Backdoor Attack—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning—0
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts—0
Understanding Community Bias Amplification in Graph Representation Learning—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning—0
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Benchmark Results

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