基于粒子群的网络社区动态角色挖掘研究
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摘要
随着网络技术和电子商务的迅猛发展,在线购物、网上支付、即时通讯等应用已经成为人们工作和生活中不可或缺的一部分。SNS的出现和发展更是为网络用户提供了一个相对绿色、安全的交流平台,然而,社会服务的实现需要用户兴趣模型的寻找与构建,因此,如何从错综复杂的关系网中发现相似用户、挖掘社区结构已成为当前社会网络研究的热点问题。
传统的社区挖掘研究以静态的观点对待社会网络分析,例如网络的度分布、边介数、聚集系数等等,而忽视了具有能动性的个体行动者。受此影响,现有的社区挖掘算法更加重视对网络拓扑结构的划分,而忽略了对社区内部成员的研究与分析,限制了网络社区概念模型的设计。本文提出应用粒子群优化算法的思想对社会网络进行结构挖掘,更加注重精英粒子的引导作用,同时结合社区内部成员的不同属性,对其进行角色划分。针对社会网络分析中存在的关键问题(动态挖掘、社区性质分析、社区成员角色划分、抗攻击性能分析等),本文基于粒子群算法算法的思想对网络社区进行了如下研究：
(1)利用动量粒子群算法实现了对社区的动态挖掘。通过对社会网络的特征矩阵进行研究,提出把Capocci算法得到的前k个非平凡特征向量Vp=(Vp1,vp2…,Vps)(P=1,…,K)作为输入,利用粒子群算法挖掘社区结构。本文选取第一个为负的特征值之前的r个非平凡特征值,以及其对应的r个非平凡特征向量,也就是预测网络有r+1个社区。但这么选择的r.一定大于等于=m一1。在此基础上,将m的选取融入编码结构中,在优化的过程中动态发现社区。粒子群算法为网络社区动态角色挖掘算法的实现提供了理论依据。
(2)结合社区的结构特征与社区内用户的属性进行分析。粒子群算法指出,种群中的粒子受到具体目标的驱动相互运动,因此,不仅是粒子近邻之间相互影响,更是因为具有同质的目标。社会网络中的个体更是如此,彼此之间的链接结构只是在表面上阐明了用户之间的显性关系,而没有从用户的性质上做进一步细分,用户的所有兴趣都表现在链接关系中,但是仅仅挖掘关系结构,并不能说明用户之间的具体联系。在社会网络中,同一用户可能不仅单对一方面感兴趣,而是可能同时对多个属性不同的事务感兴趣,由于结构挖掘把这些兴趣统一对待,因此无法区别同一用户在不同兴趣模型中的位置。结合粒子群中目标驱动的概念,从语法和语义两个层面进行社区划分,为社区定义主题。首先根据网络中节点之间的显性关系,对社区进行粗略的结构挖掘,寻找整个社会网络中存在的社区拓扑结构；然后根据社区内用户的特征向量为每个社区定义一个主题,而用户收到社区主题的驱动,交互影响。算法通过分析节点的性质与特征,优化社区划分结果,构建用户的兴趣模型和功能单元。该算法的提出为SNS科技论文管理平台的设计与实现提供了优化思想。
(3)结合粒子群算法的基本思想,受到优先情节和增长定律的启发,本文提出了种网络社区动态角色挖掘算法。根据粒子群算法中局部最优个体对种群内部个体的引导作用,创新性的提出社区种子的概念。社区种子引导社区的形成,其余个体围绕社区种子形成一个个内部联系紧密,外部联系稀疏的社区。根据优先情节和增长定律,早出现的节点要比晚出现的节点具有更多的机会积累链接,因此结合精英粒子的概念,作者开创性的提出：假设度数最大的节点最先出现在社会网络中,则可以根据节点的度数分布,以时间为轴逆向推导整个社会网络的形成演化过程；同时,在社区挖掘的过程中进行用户角色划分,实现动态角色划分的过程。将该算法应用于SNS科技论文管理平台中进行文献聚类,然后以聚类为单元进行作者分析,有利于发现同意作者在不同聚类中的地位和角色,更好的进行个性化服务。
(4)根据网络社区动态角色挖掘算法(Dynamic role assorted, DRA)和传统社区挖掘算法(G-N)进行社会网络安全保护策略的构建,引入“柔性退化”的概念,分析不同安全策略的柔性抗攻击能力。基于DRA算法的安全保护策略为社会网络中的特殊节点提供独特的防御措施,保障主要网络架构的流通性和网络的基本功能,构建“深度防御策略”限制网络攻击所造成的影响。该项策略重点体现了对网络防护的灵活性要求,在降低系统防护开销的前提下,增强了网络的稳定性和鲁棒性。将个性化安全保护策略应用于SNS科技论文管理平台,有效地提高了系统的快速重建能力和柔性抗攻击能力,使得系统能够灵活持久的保持信息流通性和安全性。
(5)搭建了一个SNS科技论文管理平台进行算法的分析与验证。在该平台中,应用启发式动态社区挖掘算法对文献网络进行聚类,根据文献聚类的结果对文献的作者构建社区；同时根据动态角色挖掘算法分析每一位作者在不同社区中的角色和地位；最后在平台中分别检测基于DRA算法和G-N算法的SNS平台的抗攻击能力。该服务平台为广大科研工作者提供一个统一的学术入口,能够集成检索特定学科或专题领域的各类分散的学术信息资源,实现个性化定制和个性化推荐的服务。平台的实验运行结果表明了网络社区动态角色挖掘算法的适应性和可扩展性,提高了系统的灵活性和稳定性。
With the development of internet technologies, e-commerce, online shopping, e-pay and immediate message (IM) have become an essential part of people's daily life. Especially the appearance and growth of SNS, it provides network users with a rather safer platform for communication. However, the implementation of good social service needs to find and construct the users'interesting model, therefore, how to find the similar users, how to discover the community structures in complex network have become a new research spot recently.
Traditional community discovery looks on social network service with static viewpoints, for instance, the distribution of node-degree, the edge betweeness, and the coefficient of concentration are used to describe the static features of social network, while ignores the motility of users. This paper comes up with the idea of using particle swarm optimization algorithm to mine the community structures, which pay more attention to the guidiance of elite particles, as well as takes the communities'inner members'distinctive attributes into accout to distribute different roles to them. In terms of the key issues in social network analysis, for example, dynamic discovery, role assort for community inner members and analysis for attack tolerance, to name but a few, this paper based on PSO to study the social network analysis and gets some great achievement as below shows.
(1) Applied momentum particle swarm algorithm to realize the dynamic discovery of community in social network. By studying the characteristic matrix of social network, the author comes up with the method of using the top k nontrivial eigenvectors get by Capocci algorithm Vp=(vp1,vp2,…,vps)(p=1,…,k) as input and making use of the PSO algorithm to mine communities. Taking the former r nontrivial eigenvalues before the first negative one, and the corresponding r nontrivial eigenvectors, which means it forecasts that the network has r+1communities. In this condition, r must not less than k=m-1. And on that basis, in order to dynamically find the communities during the optimization procedure to put the choosing process of m into the coding structure. The PSO algorithm provides for the implementation of dynamic role assorted community discovery with theoretical basis.
(2) Combined the communities'structure characteristic and the inner members' attributes together to analyze and divide communities on both grammar and semantic levels. It firstly accords to the obvious relationship between nodes in the network to roughly mine the network structures, looks for the communities topology structures in the entire social network, and then defines a theme for each community according to the community's inner members' eigenvectors; Secondly, it analyzes the inner nodes'properties and characteristics to optimize the community dividing results. This algorithm takes advantage of users'characteristics to refine the community discovery results, which is good to study and program the system's functional modules and users'interests units. This algorithm is put forward to optimize the design and implementation of SNS scientific paper management system which will be introduced at the end.
(3) Inspired by the priority complex and the grow law, the author puts forward a new dynamic community discovery algorithm based on role assorted thoughts which combines with PSO algorithm. According to the guidance of local optimal particle to the normal particles, the author innovatively comes up with the concept of community seed. The community seed dominants the formation of social communities, the rest particles located around the community seed and form a virtual community with intense inter-inter connections and sparse inter-outer connections. The priority complex and the grow law tell us that early appeared nodes have more chances to accumulate links than late appeared nodes, therefore, the author creatively proposes that:assume the node with the highest degree come to the network firstly, it is appropriate to reversely deduce the entire network's formation and evolution mechanism in accordance with the nodes'degree distribution. In the meantime, distributing roles to the community inner members during the community discovery process, and to dynamically mine community structures. In the part of the design and implementation of SNS scientific paper management, the algorithm is used to cluster papers, and then use clusters as the unit to analyze the relationship between different writers. Using this algorithm is good to find the writers' roles and status in different clusters and to provide better customized recommendation.
(4) Based on the dynamic role assorted community discovery algorithm (DRA) and traditional community discovery algorithm (G-N) to construct different kinds of protection strategies. This paper introduces the concept of "flexible degradation", analyzes the strategies' flexible ability to tolerate hostile attacks of this two kind algorithms. The customized protection strategy is able to provide special security for important nodes, then to protect the negotiability and the basic functions of the main network structure, that is to say, to construct the "deep defense strategy" to limit the destructive power of hostile attack. This strategy mainly shows the request to flexibly protect the social network; it aims at increasing the network's stability and robustness on the promise of decreasing the system's protection spending. In the next part, this strategy is applied to the construction of SNS scientific paper management system, running results show that this strategy effectively increases the system's attack tolerance and fast recovery ability, which enables the system to flexibly maintain the information's liquidity and safety.
(5) A SNS scientific paper management platform is constructed to analyze and test the suggested algorithms. This platform provides a common academic entrance for scientific research people; it is able to search all kinds of decentralized academic information resources in specific fields, to provide with customized design and recommendation services. Running results of the platform show that the suggested algorithms in this paper increase the platform's adaptability and robustness with great efficiency and flexibility.
传统的社区挖掘研究以静态的观点对待社会网络分析,例如网络的度分布、边介数、聚集系数等等,而忽视了具有能动性的个体行动者。受此影响,现有的社区挖掘算法更加重视对网络拓扑结构的划分,而忽略了对社区内部成员的研究与分析,限制了网络社区概念模型的设计。本文提出应用粒子群优化算法的思想对社会网络进行结构挖掘,更加注重精英粒子的引导作用,同时结合社区内部成员的不同属性,对其进行角色划分。针对社会网络分析中存在的关键问题(动态挖掘、社区性质分析、社区成员角色划分、抗攻击性能分析等),本文基于粒子群算法算法的思想对网络社区进行了如下研究：
(1)利用动量粒子群算法实现了对社区的动态挖掘。通过对社会网络的特征矩阵进行研究,提出把Capocci算法得到的前k个非平凡特征向量Vp=(Vp1,vp2…,Vps)(P=1,…,K)作为输入,利用粒子群算法挖掘社区结构。本文选取第一个为负的特征值之前的r个非平凡特征值,以及其对应的r个非平凡特征向量,也就是预测网络有r+1个社区。但这么选择的r.一定大于等于=m一1。在此基础上,将m的选取融入编码结构中,在优化的过程中动态发现社区。粒子群算法为网络社区动态角色挖掘算法的实现提供了理论依据。
(2)结合社区的结构特征与社区内用户的属性进行分析。粒子群算法指出,种群中的粒子受到具体目标的驱动相互运动,因此,不仅是粒子近邻之间相互影响,更是因为具有同质的目标。社会网络中的个体更是如此,彼此之间的链接结构只是在表面上阐明了用户之间的显性关系,而没有从用户的性质上做进一步细分,用户的所有兴趣都表现在链接关系中,但是仅仅挖掘关系结构,并不能说明用户之间的具体联系。在社会网络中,同一用户可能不仅单对一方面感兴趣,而是可能同时对多个属性不同的事务感兴趣,由于结构挖掘把这些兴趣统一对待,因此无法区别同一用户在不同兴趣模型中的位置。结合粒子群中目标驱动的概念,从语法和语义两个层面进行社区划分,为社区定义主题。首先根据网络中节点之间的显性关系,对社区进行粗略的结构挖掘,寻找整个社会网络中存在的社区拓扑结构；然后根据社区内用户的特征向量为每个社区定义一个主题,而用户收到社区主题的驱动,交互影响。算法通过分析节点的性质与特征,优化社区划分结果,构建用户的兴趣模型和功能单元。该算法的提出为SNS科技论文管理平台的设计与实现提供了优化思想。
(3)结合粒子群算法的基本思想,受到优先情节和增长定律的启发,本文提出了种网络社区动态角色挖掘算法。根据粒子群算法中局部最优个体对种群内部个体的引导作用,创新性的提出社区种子的概念。社区种子引导社区的形成,其余个体围绕社区种子形成一个个内部联系紧密,外部联系稀疏的社区。根据优先情节和增长定律,早出现的节点要比晚出现的节点具有更多的机会积累链接,因此结合精英粒子的概念,作者开创性的提出：假设度数最大的节点最先出现在社会网络中,则可以根据节点的度数分布,以时间为轴逆向推导整个社会网络的形成演化过程；同时,在社区挖掘的过程中进行用户角色划分,实现动态角色划分的过程。将该算法应用于SNS科技论文管理平台中进行文献聚类,然后以聚类为单元进行作者分析,有利于发现同意作者在不同聚类中的地位和角色,更好的进行个性化服务。
(4)根据网络社区动态角色挖掘算法(Dynamic role assorted, DRA)和传统社区挖掘算法(G-N)进行社会网络安全保护策略的构建,引入“柔性退化”的概念,分析不同安全策略的柔性抗攻击能力。基于DRA算法的安全保护策略为社会网络中的特殊节点提供独特的防御措施,保障主要网络架构的流通性和网络的基本功能,构建“深度防御策略”限制网络攻击所造成的影响。该项策略重点体现了对网络防护的灵活性要求,在降低系统防护开销的前提下,增强了网络的稳定性和鲁棒性。将个性化安全保护策略应用于SNS科技论文管理平台,有效地提高了系统的快速重建能力和柔性抗攻击能力,使得系统能够灵活持久的保持信息流通性和安全性。
(5)搭建了一个SNS科技论文管理平台进行算法的分析与验证。在该平台中,应用启发式动态社区挖掘算法对文献网络进行聚类,根据文献聚类的结果对文献的作者构建社区；同时根据动态角色挖掘算法分析每一位作者在不同社区中的角色和地位；最后在平台中分别检测基于DRA算法和G-N算法的SNS平台的抗攻击能力。该服务平台为广大科研工作者提供一个统一的学术入口,能够集成检索特定学科或专题领域的各类分散的学术信息资源,实现个性化定制和个性化推荐的服务。平台的实验运行结果表明了网络社区动态角色挖掘算法的适应性和可扩展性,提高了系统的灵活性和稳定性。
With the development of internet technologies, e-commerce, online shopping, e-pay and immediate message (IM) have become an essential part of people's daily life. Especially the appearance and growth of SNS, it provides network users with a rather safer platform for communication. However, the implementation of good social service needs to find and construct the users'interesting model, therefore, how to find the similar users, how to discover the community structures in complex network have become a new research spot recently.
Traditional community discovery looks on social network service with static viewpoints, for instance, the distribution of node-degree, the edge betweeness, and the coefficient of concentration are used to describe the static features of social network, while ignores the motility of users. This paper comes up with the idea of using particle swarm optimization algorithm to mine the community structures, which pay more attention to the guidiance of elite particles, as well as takes the communities'inner members'distinctive attributes into accout to distribute different roles to them. In terms of the key issues in social network analysis, for example, dynamic discovery, role assort for community inner members and analysis for attack tolerance, to name but a few, this paper based on PSO to study the social network analysis and gets some great achievement as below shows.
(1) Applied momentum particle swarm algorithm to realize the dynamic discovery of community in social network. By studying the characteristic matrix of social network, the author comes up with the method of using the top k nontrivial eigenvectors get by Capocci algorithm Vp=(vp1,vp2,…,vps)(p=1,…,k) as input and making use of the PSO algorithm to mine communities. Taking the former r nontrivial eigenvalues before the first negative one, and the corresponding r nontrivial eigenvectors, which means it forecasts that the network has r+1communities. In this condition, r must not less than k=m-1. And on that basis, in order to dynamically find the communities during the optimization procedure to put the choosing process of m into the coding structure. The PSO algorithm provides for the implementation of dynamic role assorted community discovery with theoretical basis.
(2) Combined the communities'structure characteristic and the inner members' attributes together to analyze and divide communities on both grammar and semantic levels. It firstly accords to the obvious relationship between nodes in the network to roughly mine the network structures, looks for the communities topology structures in the entire social network, and then defines a theme for each community according to the community's inner members' eigenvectors; Secondly, it analyzes the inner nodes'properties and characteristics to optimize the community dividing results. This algorithm takes advantage of users'characteristics to refine the community discovery results, which is good to study and program the system's functional modules and users'interests units. This algorithm is put forward to optimize the design and implementation of SNS scientific paper management system which will be introduced at the end.
(3) Inspired by the priority complex and the grow law, the author puts forward a new dynamic community discovery algorithm based on role assorted thoughts which combines with PSO algorithm. According to the guidance of local optimal particle to the normal particles, the author innovatively comes up with the concept of community seed. The community seed dominants the formation of social communities, the rest particles located around the community seed and form a virtual community with intense inter-inter connections and sparse inter-outer connections. The priority complex and the grow law tell us that early appeared nodes have more chances to accumulate links than late appeared nodes, therefore, the author creatively proposes that:assume the node with the highest degree come to the network firstly, it is appropriate to reversely deduce the entire network's formation and evolution mechanism in accordance with the nodes'degree distribution. In the meantime, distributing roles to the community inner members during the community discovery process, and to dynamically mine community structures. In the part of the design and implementation of SNS scientific paper management, the algorithm is used to cluster papers, and then use clusters as the unit to analyze the relationship between different writers. Using this algorithm is good to find the writers' roles and status in different clusters and to provide better customized recommendation.
(4) Based on the dynamic role assorted community discovery algorithm (DRA) and traditional community discovery algorithm (G-N) to construct different kinds of protection strategies. This paper introduces the concept of "flexible degradation", analyzes the strategies' flexible ability to tolerate hostile attacks of this two kind algorithms. The customized protection strategy is able to provide special security for important nodes, then to protect the negotiability and the basic functions of the main network structure, that is to say, to construct the "deep defense strategy" to limit the destructive power of hostile attack. This strategy mainly shows the request to flexibly protect the social network; it aims at increasing the network's stability and robustness on the promise of decreasing the system's protection spending. In the next part, this strategy is applied to the construction of SNS scientific paper management system, running results show that this strategy effectively increases the system's attack tolerance and fast recovery ability, which enables the system to flexibly maintain the information's liquidity and safety.
(5) A SNS scientific paper management platform is constructed to analyze and test the suggested algorithms. This platform provides a common academic entrance for scientific research people; it is able to search all kinds of decentralized academic information resources in specific fields, to provide with customized design and recommendation services. Running results of the platform show that the suggested algorithms in this paper increase the platform's adaptability and robustness with great efficiency and flexibility.
引文
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