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 191–200 of 982 papers

TitleStatusHype
Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks—0
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDCode1
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks—0
GraphMatcher: A Graph Representation Learning Approach for Ontology MatchingCode0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
CORE: Data Augmentation for Link Prediction via Information Bottleneck—0
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction—0
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs—0
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks—0
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Benchmark Results

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