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 426–450 of 1570 papers

TitleStatusHype
Multi-Scale Graph Learning for Anti-Sparse Downscaling—0
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach—0
Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling—0
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs—0
GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learningCode0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Edge-boosted graph learning for functional brain connectivity analysis—0
FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning—0
EG-Gaussian: Epipolar Geometry and Graph Network Enhanced 3D Gaussian Splatting—0
Plain Transformers Can be Powerful Graph Learners—0
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Trajectory Encoding Temporal Graph NetworksCode0
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures—0
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives—0
Federated Prototype Graph Learning—0
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph LearningCode0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Scalable Hypergraph Structure Learning with Diverse Smoothness PriorsCode0
Toward General and Robust LLM-enhanced Text-attributed Graph Learning—0
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution—0
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive ReviewCode0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified