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 401–410 of 982 papers

TitleStatusHype
Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks—0
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
Graph Neural Networks for Binary Programming—0
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Benchmark Results

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