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 401–450 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
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks—0
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs—0
Graph Neural Networks for Binary Programming—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs EmbeddingCode0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification—0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Spatial-Temporal Graph Representation Learning for Tactical Networks Future State PredictionCode0
Graph Partial Label Learning with Potential Cause Discovering—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Robust Graph Structure Learning under Heterophily—0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease—0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Negative Sampling in Knowledge Graph Representation Learning: A Review—0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Representation learning in multiplex graphs: Where and how to fuse information?Code0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Graph Mamba: Towards Learning on Graphs with State Space ModelsCode0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient MatchingCode0
On provable privacy vulnerabilities of graph representations—0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques—0
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent SamplingCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Graph Transformers without Positional Encodings—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Product Manifold Representations for Learning on Biological PathwaysCode0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment DecodingCode0
Graph Representation Learning for Contention and Interference Management in Wireless NetworksCode0
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Benchmark Results

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