融合多粒度社区信息的网络嵌入方法

现有大多数网络嵌入方法仅保留了网络的局部结构信息，而忽略了网络中的其他潜在信息。为了保留网络的社区信息，并体现网络社区结构的多粒度特性，提出一种融合多粒度社区信息的网络嵌入方法（EMGC）。首先，获得网络的多粒度社区结构，并初始化节点嵌入和社区嵌入；然后，根据上一粒度上的节点嵌入和本层粒度的社区结构，更新社区嵌入，进而调整相应的节点嵌入；最后，对不同粒度下的节点嵌入进行拼接，从而得到融合多粒度社区信息的网络嵌入结果。在4个真实网络数据集上进行实验，相较于未考虑社区信息的方法（DeepWalk、node2vec）和考虑了单一粒度社区信息的方法（ComE、GEMSEC），EMGC在链接预测上的AUC值和节点分类上的F1值总体上优于对比方法。实验结果表明EMGC能够有效提升后续链接预测和节点分类的准确率。

Network embedding method based on multi-granularity community information

Most of the existing network embedding methods only preserve the local structure information of the network， while they ignore other potential information in the network. In order to preserve the community information of the network and reflect the multi-granularity characteristics of the network community structure， a network Embedding method based on Multi-Granularity Community information （EMGC） was proposed. Firstly， the network’s multi-granularity community structure was obtained， the node embedding and the community embedding were initialized. Then， according to the node embedding at previous level of granularity and the community structure at this level of granularity， the community embedding was updated， and the corresponding node embedding was adjusted. Finally， the node embeddings under different community granularities were spliced to obtain the network embedding that fused the community information of different granularities. Experiments on four real network datasets were carried out. Compared with the methods that do not consider community information （DeepWalk， node2vec） and the methods that consider single-granularity community information （ComE， GEMSEC）， EMGC’s AUC value on link prediction and F1 score on node classification are generally better than those of the comparison methods. The experimental results show that EMGC can effectively improve the accuracy of subsequent link prediction and node classification.