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 226–250 of 982 papers

TitleStatusHype
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
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
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
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent SamplingCode0
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Graph Domain Adaptation: Challenges, Progress and ProspectsCode2
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
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