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 851–875 of 1570 papers

TitleStatusHype
Psycholinguistic Tripartite Graph Network for Personality Detection—0
PuzzleNet: Scene Text Detection by Segment Context Graph Learning—0
Computing Steiner Trees using Graph Neural Networks—0
Quantum Graph Learning: Frontiers and Outlook—0
Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Connecting the Dots: Identifying Network Structure via Graph Signal Processing—0
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack—0
Recognizing Multimodal Entailment—0
Recognizing Predictive Substructures with Subgraph Information Bottleneck—0
Recommending on graphs: a comprehensive review from a data perspective—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model—0
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction—0
Regularized Primitive Graph Learning for Unified Vector Mapping—0
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment—0
Reinforced Imitative Graph Learning for Mobile User Profiling—0
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning—0
Relational Graph Learning for Grounded Video Description Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified