网络形式背景下的社区划分方法研究

Research on Community Division Method Under Network Formal Context

网络社区划分是从社会网络中进行概念认知、模式学习的基础,也是网络背景下机器学习研究的热点问题。为了充分发挥形式概念和网络特征值的双重优势,基于网络形式背景研究网络社区划分问题。首先,将网络结构与节点属性信息相结合给出了网络节点中心度和中心势,使得网络形式背景的网络社区划分综合考虑了网络结构和节点内涵两方面的特征;其次,提出了网络形式背景的网络社区概念,该概念不仅给出了传统形式背景的形式概念,还包含了概念的网络特征值,可以描述该概念在网络中的平均重要度和平均重要度势差;然后,考虑到社会网络划分中多角色与网络有向性的特点,又将有向网络分为单角色网络和双角色网络,运用网络结构与节点属性信息相结合的方法提出了两种网络社区划分算法,并分析了算法的时间复杂度;最后,通过实例说明了上述网络社区划分算法的有效性。所得结论为网络数据挖掘和网络概念认知的进一步研究提供了参考。

Network community division is the basis of concept cognition and pattern learning from social networks,and is also a hot topic in the study of machine learning under the network background.In order to make full use of the advantages of formal concepts and network characteristic values,this paper discusses the problem of network community division based on network formal contexts.It firstly gives the notions of network node centrality and centralization based on the information of network structure and node attributes,which makes the division of network community in a network formal context take into account the characteristics of the network structure and node connotation.Then,the network community concept of network formal context is presented.It not only obtains the formal concept of a traditional formal context,but also includes the network characteristic values of the concept.As a result,the average importance of the concept in the network can be described as well as the different quantity between the average importances.Furthermore,considering the characteristics of multiple roles and network orienta-tion in the division of a social network,the directed network is divided into single-role network and double-role network.Besides,two network community division algorithms are proposed by combining the information of network structure and node attributes,and their time complexities are analyzed.Finally,examples are used to show the effectiveness of the proposed network community division algorithms.The obtained rusults can provide a reference for the further study of network data mining and network concept cognition.