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 581–590 of 982 papers

TitleStatusHype
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
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction—0
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Benchmark Results

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