融合图卷积网络模型的无监督社区检测算法

图卷积神经网络（Graph Convolutional Neural Network）能有效地提取非欧式距离数据中的特征信息。提出一种基于图卷积网络模型的无监督社区检测算法。选择图中某些节点添加人工标签来模拟在图上的信号输入，使其满足图卷积网络的传播特征的要求，通过修改后的图卷积网络传播规则将节点本身的标签传递至其相邻节点，通过对同一节点获得的不同标签进行比较后将节点归类，之后优化归类结果并输出社区划分矩阵。使用现实世界的数据集进行测试，并与一些其他社区检测算法进行对比评估。实验结果表明算法在不同类型的数据集中都能得到很好的社区划分效果。

Unsupervised Community Detection Algorithm Integrating Graph Convolutional Network Model

The graph convolutional neural network can effectively extract feature information on non-Euclidean distance data. This paper proposes an unsupervised community detection algorithm based on graph convolutional network model. The algorithm first selects some nodes in the graph to add artificial flags to simulate the input signal, satisfying the requirements of the propagation rule of the graph convolutional network. Then it passes the flags to the neighboring nodes through the modified graph convolutional network propagation rule. While comparing the different flags on the same node and getting the attribution result of the nodes, community partitioning matrix can be obtained by optimizing the result. Finally, it uses real-world data sets to test this algorithm and compares with some classic community detection algorithms. The experimental results show that the algorithm can achieve a better result in different types of graph data sets.