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 571–580 of 982 papers

TitleStatusHype
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
Uplifting Message Passing Neural Network with Graph Original Information—0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention—0
Causal Machine Learning: A Survey and Open Problems—0
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Benchmark Results

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