This is a PyTorch implementation of the paper Graph Attention Networks.
\nGATs work on graph data. A graph consists of nodes and edges connecting nodes. For example, in Cora dataset the nodes are research papers and the edges are citations that connect the papers.
\nGAT uses masked self-attention, kind of similar to transformers. GAT consists of graph attention layers stacked on top of each other. Each graph attention layer gets node embeddings as inputs and outputs transformed embeddings. The node embeddings pay attention to the embeddings of other nodes it's connected to. The details of graph attention layers are included alongside the implementation.
\nHere is the training code for training a two-layer GAT on Cora dataset.
\n": "\u3053\u308c\u306f\u8ad6\u6587\u306e\u300c\u30b0\u30e9\u30d5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u300d\u306e PyTorch \u5b9f\u88c5\u3067\u3059\u3002
\nGAT \u306f\u30b0\u30e9\u30d5\u30c7\u30fc\u30bf\u3092\u51e6\u7406\u3057\u307e\u3059\u3002\u30b0\u30e9\u30d5\u306f\u3001\u30ce\u30fc\u30c9\u3068\u30ce\u30fc\u30c9\u3092\u63a5\u7d9a\u3059\u308b\u30a8\u30c3\u30b8\u3067\u69cb\u6210\u3055\u308c\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001Cora\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306f\u3001\u30ce\u30fc\u30c9\u306f\u7814\u7a76\u8ad6\u6587\u3067\u3001\u7aef\u306f\u8ad6\u6587\u3092\u3064\u306a\u3050\u5f15\u7528\u3067\u3059
\u3002\nGAT\u306f\u3001\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u306b\u4f3c\u305f\u3001\u30de\u30b9\u30af\u3055\u308c\u305f\u30bb\u30eb\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092\u4f7f\u3044\u307e\u3059\u3002GAT\u306f\u3001\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc\u304c\u4e92\u3044\u306b\u91cd\u306a\u308a\u5408\u3063\u3066\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u5404\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc\u306f\u3001\u5165\u529b\u3068\u3057\u3066\u30ce\u30fc\u30c9\u57cb\u3081\u8fbc\u307f\u3092\u53d6\u5f97\u3057\u3001\u5909\u63db\u3055\u308c\u305f\u57cb\u3081\u8fbc\u307f\u3092\u51fa\u529b\u3057\u307e\u3059\u3002\u30ce\u30fc\u30c9\u57cb\u3081\u8fbc\u307f\u306f\u3001\u63a5\u7d9a\u3055\u308c\u3066\u3044\u308b\u4ed6\u306e\u30ce\u30fc\u30c9\u306e\u57cb\u3081\u8fbc\u307f\u306b\u6ce8\u76ee\u3057\u307e\u3059\u3002\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc\u306e\u8a73\u7d30\u306f\u3001\u5b9f\u88c5\u3068\u3068\u3082\u306b\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002
\n\n", "Graph Attention Networks (GAT)": "\u30b0\u30e9\u30d5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (GAT)" }