The full form of GCN is Graph Convolutional Network.
Graph Convolutional Networks are a powerful class of neural networks designed to operate on graph-structured data. They extend the concept of convolution from traditional convolutional neural networks (CNNs), commonly used for image processing, to handle the irregular and complex nature of graphs.
Here's a breakdown of what that means:
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Graph: A data structure consisting of nodes (vertices) and edges (connections) that represent relationships between the nodes. Examples include social networks, citation networks, and molecular structures.
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Convolutional: In the context of neural networks, a convolution is a mathematical operation that extracts features from input data by applying a filter (or kernel) across it. In CNNs, this involves sliding a filter over an image to detect patterns.
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Network: In this case, a neural network, which is a computational model inspired by the structure and function of the human brain. GCNs use layers of interconnected nodes (neurons) to learn representations from graph data.
In essence, a GCN learns node embeddings by aggregating feature information from a node's neighbors. This aggregation process is repeated across multiple layers, allowing the network to capture higher-order relationships and dependencies within the graph. GCNs are widely used in various applications, including:
- Node classification: Predicting the category or label of a node.
- Link prediction: Predicting the existence of edges between nodes.
- Graph classification: Classifying entire graphs into different categories.
- Recommender systems: Suggesting items or content based on user preferences and connections.
