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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 982 papers

TitleStatusHype
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic GraphsCode1
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks—0
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
GCondenser: Benchmarking Graph CondensationCode1
Graphlets correct for the topological information missed by random walks—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation LearningCode1
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Temporal Graph ODEs for Irregularly-Sampled Time SeriesCode1
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
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