Graph Learning
462 papers with code • 1 benchmarks • 8 datasets
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.
Libraries
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Latest papers
Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
By making an assumption that the learned neighbor graph of each view comprises both a consistent graph and a view-specific graph, we formulate a new tensor-based target graph learning paradigm.
Segment Anything Model for Road Network Graph Extraction
We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) for extracting large-scale, vectorized road network graphs from satellite imagery.
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
These optimal features typically depend on tunable parameters of the lower-level energy in such a way that the entire bilevel pipeline can be trained end-to-end.
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
The proposed GCN-SA contains two enhancements corresponding to edges and node features.
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Specifically, to address the data heterogeneity across domains, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features.
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices.
OpenGraph: Towards Open Graph Foundation Models
By effectively capturing the graph's underlying structure, these GNNs have shown great potential in enhancing performance in graph learning tasks, such as link prediction and node classification.
HiGPT: Heterogeneous Graph Language Model
However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets.
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns
The opioid crisis has been one of the most critical society concerns in the United States.