Graph Learning
473 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.
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Latest papers
Grasper: A Generalist Pursuer for Pursuit-Evasion Problems
Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks.
Model Selection with Model Zoo via Graph Learning
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i. e., model zoos.
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
DE-HNN: An effective neural model for Circuit Netlist representation
Using the input and output data of the tools from past designs, one can attempt to build a machine learning model that predicts the outcome of a design in significantly shorter time than running the tool.
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
The LTS-CG leverages agents' historical observations to calculate an agent-pair probability matrix, where a sparse graph is sampled from and used for knowledge exchange between agents, thereby simultaneously capturing agent dependencies and relation uncertainty.
MAPL: Model Agnostic Peer-to-peer Learning
Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature.
Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering.
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.
Forward Learning of Graph Neural Networks
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
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.