摘要
针对现有的社区发现算法难以解决网络的多维性问题的现象,提出一种基于网络表示学习的非单一维度的社区发现算法。该算法从节点属性特征和网络结构特征两个维度考虑节点的差异性,首先根据节点属性相似度计算得到节点转移概率,结合小世界模型的六度分离理论设置网络节点随机游走路径的长度。依据转移概率选择节点的邻居节点,得到节点的游走路径,然后用神经网络模型训练节点的游走路径得到节点的网络特征向量,将节点网络特征向量的相似度重置为节点连接边的权重,在Louvain算法的基础上完成社区划分。最后,在Facebook和Giraffe两个数据集上进行了实验,选用基于初始网络结构的Louvain算法和基于单一维度的社区发现算法作为对比算法。实验结果表明,在Giraffe数据集中,相比于Louvain算法,基于节点属性的社区发现算法的模块度指标提升了2.7%,基于网络结构的社区发现算法的模块度指标提升了3.0%,提出的非单一维度的社区发现算法的模块度指标提升了3.7%。所提算法聚焦于网络的多维性,有效提升了社区发现算法的模块度。
Focusing on the issue that it is difficult for the existing community detection algorithms to solve the multidimensionality problem of the network, a non-unidimensional community detection algorithm based on network representation learning was proposed. The algorithm considered the difference of nodes from the two dimensions of node attribute feature and network structure feature. Firstly, the node transition probability was calculated according to the node attribute similarity. The length of the random walk path of the network node was set according to the six-degree separation theory of the small world model. After obtaining the walking path of the node by selecting its neighbor nodes according to the transition probability, the walking path of the node was trained by the neural network model to achieve the network feature vectors. The similarity of the network feature vectors of the node was reset as the weight of the connected edge, and the community partition was completed based on the Louvain algorithm. Finally, experiments were conducted on two datasets, Facebook and Giraffe with the Louvain algorithm based on the initial network structure and the unidimensional community detection algorithm as comparison algorithms. Experimental results show that on the Giraffe dataset, compared to the Louvain algorithm, the community detection algorithm based on the node attribute has the modularity increased by 2.7%, the community detection algorithm based on the network structure has the modularity increased by 3.0%, and the proposed non-unidimensional community detection algorithm has the modularity increased by 3.7%. The proposed algorithm focuses on the multidimensionality of the network and improves the modularity of the community detection algorithm effectively.
作者
陈婉杰
盛益强
CHEN Wanjie;SHENG Yiqiang(National Network New Media Engineering Research Center(Institute of Acoustics,Chinese Academy of Sciences),Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《计算机应用》
CSCD
北大核心
2019年第12期3467-3475,共9页
journal of Computer Applications
基金
中国科学院战略性科技先导专项课题(XDC02070100)~~
关键词
节点属性
网络结构
可扩展性
社区发现
网络表示学习
节点差异性
node attribute
network structure
extensibility
community detection
network representation learning
node difference
