GCN(Graph Convolutional Networks) and Graph-Embedding are two different methods for learning graph-structured data.
GCN(Graph Convolutional Networks) vs Graph-Embedding
In GCN, the training data consists of an adjacency matrix, a degree matrix, and the features of the nodes(vertices) in the graph. The adjacency matrix is a square matrix containing information about the connections between the nodes in the graph. The degree matrix is a diagonal matrix containing the degree of each node in the graph. The GCN model uses these matrices and the node features to learn a representation of the graph.
In Graph-Embedding, the training data consists of a set of nodes and the connections between them. The goal of graph-embedding is to learn a low-dimensional representation of the nodes in the graph such that the connections between the nodes are preserved in the low-dimensional space.
GCN is better than graph-embedding in that it is able to incorporate the structural information in the graph (given by the adjacency and degree matrices) into the learned representation, whereas graph-embedding only uses the connections between nodes. This allows GCN to learn more informative representations of the graph.
As for the difference between GCN and graph embedding, GCN is a type of neural network that operates directly on the graph structure, while graph embedding is a method for learning low-dimensional vector representations of nodes in a graph. GCN has been shown to be effective for semi-supervised learning tasks on graph-structured data, while graph embedding can be useful for a variety of downstream tasks such as visualization, clustering, and classification. The choice of which method to use depends on the specific task at hand.
Training a GCN (Graph Convolutional Network) typically involves defining a graph convolutional layer, which takes in a graph and produces a new graph with updated node features. This layer can be trained using a variant of backpropagation, where the error is propagated through the graph structure.
- Training a GCN typically involves the following steps:
- Construct the adjacency matrix and degree matrix for the graph.
- Use the adjacency matrix and degree matrix to define the GCN layer.
- Initialize the weights of the GCN layer.
- Forward propagate the input features through the GCN layer to get the output features.
- Compare the output features with the ground truth labels and compute the loss.
- Backpropagate the loss to update the weights of the GCN layer.
- Repeat steps 4-6 for multiple epochs until the model converges.
GCN and graph embedding are not necessarily related or competing techniques - they are used for different purposes and can be used together in some applications. GCN is a method for learning node representations in a graph that are suitable for performing downstream tasks, such as node classification or graph classification, while graph embedding is a method for representing nodes in a low-dimensional space for visualization or analysis.