Note: This work will be related to GNN in future.
Graph Neural Networks (GNNs) are a type of neural network designed to operate on graph data structures. In contrast to other deep learning approaches that process data in tabular or sequential forms, GNNs can capture dependencies between entities in arbitrary graph structures, including social networks, knowledge graphs, and molecular structures.
At a high level, GNNs operate by propagating information through graph edges and nodes to learn representations of the nodes that incorporate information from their local graph neighborhoods. This information propagation is typically achieved by applying a series of graph convolutional operations that update the hidden representations of nodes by aggregating and transforming information from their neighbors.
One of the key challenges in GNNs is designing effective graph convolutional operations that can capture the complex and variable interactions between nodes in different graph topologies. A number of recent advances in GNN research have explored different approaches to this problem, including attention-based mechanisms, graph attention networks, and graph attention convolutional networks.
GNNs have shown promising results in a variety of applications, including node classification, link prediction, and graph classification. Some specific examples of GNN applications include predicting protein interactions, identifying communities in social networks, and detecting fraud in financial transaction networks.
Overall, GNNs represent an exciting and rapidly developing area of research in deep learning, with many opportunities for future development and application.