一种度修正的属性网络随机块模型

郑忆美; 贾彩燕; 常振海; 李轩涯

doi:10.7544/issn1000-1239.2020.20200158

一种度修正的属性网络随机块模型

¹(北京交通大学计算机与信息技术学院北京 100044)
²(交通数据分析与挖掘北京市重点实验室(北京交通大学) 北京 100044)
³(天水师范学院数学与统计学院甘肃天水 741000)
⁴(百度在线网络技术(北京)有限公司北京 100085) (ymzheng@bjtu.edu.cn)

基金项目: 国家自然科学基金项目(61876016，61632004)；中央高校基本科研业务费专项资金项目(2019JBZ110)；百度松果计划开放研究基金项目

详细信息

中图分类号: TP301
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出版历程
- 发布日期: 2020-07-31

A Degree Corrected Stochastic Block Model for Attributed Networks

¹(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044)
²(Beijing Key Laboratory of Traffic Data Analysis and Mining(Beijing Jiaotong University), Beijing 100044)
³(School of Mathematics and Statistics, Tianshui Normal University, Tianshui, Gansu 741000)
⁴(Baidu Online Network Technology (Beijing) Co., Ltd, Beijing 100085)

Funds: This work was supported by the National Natural Science Foundation of China (61876016, 61632004), the Fundamental Research Funds for the Central Universities (2019JBZ110), and the Baidu Pinecone Program.

摘要

摘要: 社区检测是复杂网络分析中的重要任务，现有的社区检测方法多侧重于利用单纯的网络结构，而融合节点属性的方法也主要针对传统的社区结构，不能检测网络中的二部图结构、混合结构等情况.此外，网络中每个节点的度会影响网络中链接的构成，同样会影响社区结构的分布.因此，提出一种基于随机块模型的属性网络社区检测方法DPSB_PG.不同于其他属性网络中的生成式模型，该方法中节点链接和节点属性的产生均服从泊松分布，并基于随机块模型考虑社区间相连接的概率，重点在节点链接的生成过程中融合度修正的思想，最后利用期望最大化EM算法推断模型中的参数，得到网络中节点的社区隶属度.真实网络上的实验结果显示：模型继承了随机块模型的优点，能够检测网络中的广义社区结构，且由于度修正的引入，具有很好的数据拟合能力，因此在属性网络与非属性网络社区检测性能上优于其他现有相关算法.
- 度修正 /
- 泊松分布 /
- 随机块模型 /
- 广义结构 /
- 属性网络
Abstract: Community detection is an important task in complex network analysis. The existing community detection methods mostly focus on utilizing the simple network structure, while the methods of integrating network topology and node attributes are also mainly aimed at the traditional community structure, which fails to detect the bipartite structure, mixed structure, etc. However, the degree of each node in the network will affect the composition of the links in the network, as well as the distribution of the community structure. This paper proposes a method called DPSB_PG for attributed networks community detection based on the stochastic block model. Unlike other generative models for attributed networks, in this method, the generation of node links and node attributes both followes the Poisson distribution, and considers the probability between communities based on the stochastic block model. Moreover, the idea of degree corrected is integrated in the process of generating node links. Finally, in order to obtain the community membership of nodes, the expectation-maximization algorithm is used to infer the parameters of the model. The experimental results on the real networks show that the DPSB_PG inherits the advantages of the stochastic block model and can detect the general community structure in networks. Since the introduction of the idea of degree corrected, this model has a good data fitting ability. Overall, the performance of this model is superior to other existing state-of-the-art community detection algorithms for both attributed networks and non-attributed networks.
- degree corrected /
- Poisson distribution /
- stochastic block model /
- general structure /
- attributed networks

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