结合社区嵌入和节点嵌入的社区发现算法

Community Detection Algorithm Combing Community Embedding and Node Embedding

社区作为社交网络的重要属性,对理解网络功能和预测演化有着重要作用。通过网络嵌入将网络节点转化成低维稠密的特征向量,并将其应用于社区发现等机器学习任务,是近年来的研究热点。传统的网络嵌入方法仅针对节点嵌入,忽略了社区嵌入的重要性。针对这样的问题,提出了将社区嵌入和改进的节点嵌入相结合的方法CNE,从而获得融合结构信息和属性信息的节点表示。节点嵌入将节点表示为低维向量,类似地,社区嵌入把社区表示为低维空间中的高斯分布,二者将多种节点相似性相结合,互相促进,从而获得更为准确的社区发现结果。在公开数据集上将所提算法与传统的社区发现算法和网络嵌入方法进行比较,实验结果表明提出的CNE方法具有更高的精度。

As an important property of social networks,community plays an important role in understanding network functions and predicting evolution.It is a research hotspot in recent years to transform network nodes into low-dimensional dense feature vectors through network embedding and apply them to machine learning tasks such as community detection.The traditional network embedding method only focuses on node embedding and ignores the importance of community embedding.Aiming at such a problem,CNE,a method combining Community embedding and improved Node Embedding,is proposed to obtain node representation combining structure information and attribute information.Node embedding represents nodes as low-dimensional vectors.Similarly,community embedding represents communities as Gaussian distributions in low-dimensional spaces.They combine multiple node similarities to promote more accurate community detection results.The experimental results show that,compared with the traditional community detection algorithm and network embedding method on public datasets,the proposed CNE method has higher precision.