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 31–40 of 1570 papers

TitleStatusHype
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
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Learning Individual Behavior in Agent-Based Models with Graph Diffusion NetworksCode0
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
Using Large Language Models to Tackle Fundamental Challenges in Graph Learning: A Comprehensive SurveyCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified