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 501–525 of 1570 papers

TitleStatusHype
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
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive LearningCode0
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
Structure-Preference Enabled Graph Embedding Generation under Differential PrivacyCode0
Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image—0
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
Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information—0
FedSPA: Generalizable Federated Graph Learning under Homophily HeterogeneityCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified