An algorithm for identifying key nodes of invasive plant distribution and climate factor networks based on the GAT model

Aiming at the shortcomings of traditional methods in assessing the importance of network nodes, this paper proposes a key node identification algorithm based on graph attention network (GAT) to identify key nodes in the invasive plant network. Firstly, a complex network with invasive plant distribution points as nodes and climate factors as connecting edges is constructed, and the interaction between invasive plants and climate factors is portrayed by using the adjacency matrix. Secondly, a multi-dimensional assessment of the importance of nodes was achieved by introducing the linkage entropy weights, and considering the direct connection strength of the nodes, the complexity of the distribution of neighbourhood information and the connection weights. Finally, the weights are used to optimally train the GAT model to identify the key nodes in the network. Experimental validation shows that the algorithm outperforms the traditional method in terms of recognition accuracy, precision, recall and F1-score. The results of key node identification show that with climate change, the key nodes in Yunnan move from Kunming and Dali to Shangri-La, indicating that the key areas of plant distribution are expanding to the northwest. Accordingly, effective management of these key nodes to inhibit the spread of invasive plants can provide an important reference for ecological protection in Yunnan and the wider region.