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 871–880 of 982 papers

TitleStatusHype
On the Interpretability and Evaluation of Graph Representation Learning—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
Optimizing Supply Chain Networks with the Power of Graph Neural Networks—0
Pair-view Unsupervised Graph Representation Learning—0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks—0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning—0
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Benchmark Results

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