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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 1570 papers

TitleStatusHype
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design—0
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning—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
Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of ThingsCode0
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph LearningCode0
Spatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking—0
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
Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information—0
GraphI2P: Image-to-Point Cloud Registration with Exploring Pattern of Correspondence via Graph Learning—0
FedSPA: Generalizable Federated Graph Learning under Homophily HeterogeneityCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified