In the field of GNN (Graph Neural Network, graph neural network), there are many common model variants and extensions. Here are a few common GNN variants:
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Graph Convolutional Network (GCN): GCN is one of the earliest proposed graph neural network models. It updates node representations through neighbor aggregation and graph convolution operations, aggregating a node's features with those of its neighbors. GCN has locality in the information propagation of graph structure and can capture the neighbor information of nodes.
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GraphSAGE: GraphSAGE is a graph neural network model based on neighbor sampling. It updates node representations by sampling neighbor nodes in each layer and then aggregating neighbors' features. GraphSAGE adopts an adaptive neighbor sampling strategy and has better scalability.
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Graph Attention Network (GAT): GAT is a graph neural network model that uses an attention mechanism. It dynamically learns the weights between nodes and neighbor nodes by introducing an attention mechanism, so as to better capture important relationships between nodes. GAT performs well on node classification and graph classification tasks in graph structures.
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Variants of GraphSAGE (such as Mean Aggregator, GCN Aggregator, etc.): The original version of GraphSAGE uses aggregation functions to aggregate neighbor features. The variants of GraphSAGE extend the model by changing the aggregation function. For example, Mean Aggregator aggregates neighbor features using average values, and GCN Aggregator aggregates neighbor features using GCN operations.
In addition to the above model variants, there are other common GNN models, such as Graph Isomorphism Network (GIN), Graph Neural Networks with Edge-Conditioned Convolutions (ECC), Diffusion Convolutional Recurrent Neural Network (DCRNN), etc. These models all show good performance in different application scenarios and tasks.
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It is necessary to select a suitable GNN model and variant according to the characteristics of the specific task and the structure of the data. At the same time, for different models, it is recommended to read related papers and research articles to gain a deep understanding of their principles and performance.
