SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

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Showing 151–200 of 1570 papers

TitleStatusHype
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning—0
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design—0
Random Walk Guided Hyperbolic Graph DistillationCode0
Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach—0
A Unified Invariant Learning Framework for Graph ClassificationCode0
Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model—0
Each Graph is a New Language: Graph Learning with LLMs—0
Spatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking—0
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph LearningCode0
Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of ThingsCode0
A Simple Graph Contrastive Learning Framework for Short Text ClassificationCode1
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive LearningCode0
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning—0
Active Sampling for Node Attribute Completion on Graphs—0
A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks—0
Graph Contrastive Learning on Multi-label Classification for Recommendations—0
Graph-Based Multimodal and Multi-view Alignment for Keystep RecognitionCode2
Structure-Preference Enabled Graph Embedding Generation under Differential PrivacyCode0
Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image—0
Long-range Brain Graph TransformerCode1
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning—0
GraphI2P: Image-to-Point Cloud Registration with Exploring Pattern of Correspondence via Graph Learning—0
FedSPA: Generalizable Federated Graph Learning under Homophily HeterogeneityCode0
Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information—0
AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing GraphsCode0
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution—0
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction—0
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations—0
Causal Discovery on Dependent Binary Data—0
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining—0
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters—0
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph LearningCode0
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics—0
FedGIG: Graph Inversion from Gradient in Federated Learning—0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges—0
THeGCN: Temporal Heterophilic Graph Convolutional Network—0
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks—0
GraphSeqLM: A Unified Graph Language Framework for Omic Graph LearningCode0
Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal RecommendationCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal RecommendationCode2
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and TasksCode0
SPGL: Enhancing Session-based Recommendation with Single Positive Graph LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified