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 301–350 of 982 papers

TitleStatusHype
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
From ChebNet to ChebGibbsNetCode0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Perturbation Ontology based Graph Attention Networks—0
Instance-Aware Graph Prompt Learning—0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Conditional Distribution Learning on GraphsCode0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Shedding Light on Problems with Hyperbolic Graph Learning—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Variational Graph Contrastive LearningCode0
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning—0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach—0
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN—0
Sparse Decomposition of Graph Neural Networks—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
Towards Fair Graph Representation Learning in Social Networks—0
Querying functional and structural niches on spatial transcriptomics dataCode0
Information propagation dynamics in Deep Graph Networks—0
A Benchmark on Directed Graph Representation Learning in Hardware Designs—0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks—0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
PROXI: Challenging the GNNs for Link PredictionCode0
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process—0
TopER: Topological Embeddings in Graph Representation Learning—0
Whole-Graph Representation Learning For the Classification of Signed NetworksCode0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
Multi-object event graph representation learning for Video Question Answering—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
Debiasing Graph Representation Learning based on Information Bottleneck—0
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Neural Spacetimes for DAG Representation Learning—0
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Benchmark Results

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