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 376–400 of 1570 papers

TitleStatusHype
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution—0
Dynamic Graph Condensation—0
Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and BenchmarkCode0
Collaborative Interest-aware Graph Learning for Group Identification—0
TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure AttacksCode0
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics—0
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning—0
Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning—0
Towards Multi-modal Graph Large Language Model—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy EnvironmentsCode0
Masked Language Models are Good Heterogeneous Graph GeneralizersCode0
Nonlinear Causal Discovery for Grouped Data—0
Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction IdentificationCode0
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
Learning Individual Behavior in Agent-Based Models with Graph Diffusion NetworksCode0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Language Model-Enhanced Message Passing for Heterophilic Graph Learning—0
Interpretable Graph Learning Over Sets of Temporally-Sparse Data—0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified