Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.     

A Heuristic Clustering Algorithm for Mining Communities in Signed Networks

Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks. So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures. Finally, it is insensitive to its built-in parameters and requires no prior knowledge.     

Community structure detection in social networks based on dictionary learning

Discovering community structures is a fundamental problem concerning how to understand the topology and the functions of complex network. In this paper, we propose how to apply dictionary learning algorithm to community structure detection. We present a new dictionary learning algorithm and systematically compare it with other state-of-the-art models/algorithms. The results show that the proposed algorithm is highly effectively at finding the community structures in both synthetic datasets, including three types of data structures, and real world networks coming from different areas.     

Discovering Typed Communities in Mobile Social Networks

Mobile social networks,which consist of mobile users who communicate with each other using cell phones,are reflections of people’s interactions in social lives.Discovering typed communities(e.g.,family communities or corporate communities) in mobile social networks is a very promising problem.For example,it can help mobile operators to determine the target users for precision marketing.In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users.We use the user logs stored by mobile operators,including communication and user movement records,to collectively label all the relationships in a network,by employing an undirected probabilistic graphical model,i.e.,conditional random fields.Then we use two methods to discover typed communities based on the results of relationship labeling:one is simply retaining or cutting relationships according to their labels,and the other is using sophisticated weighted community detection algorithms.The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.     

Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.     

Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks

In this paper,we propose a balanced multi-label propagation algorithm(BMLPA) for overlapping community detection in social networks.As well as its fast speed,another important advantage of our method is good stability,which other multi-label propagation algorithms,such as COPRA,lack.In BMLPA,we propose a new update strategy,which requires that community identifiers of one vertex should have balanced belonging coefficients.The advantage of this strategy is that it allows vertices to belong to any number of communities without a global limit on the largest number of community memberships,which is needed for COPRA.Also,we propose a fast method to generate "rough cores",which can be used to initialize labels for multi-label propagation algorithms,and are able to improve the quality and stability of results.Experimental results on synthetic and real social networks show that BMLPA is very efficient and effective for uncovering overlapping communities.     

Towards Effective Integrated Access Control Lists in Internet Networks

ACLs （access control lists） represent a traditional way in filtering packets in routers. In modern complex enterprise networks that provide a vast array of services, there is an ever increasing need for verifying the integrity of ACLs to detect any potential security holes and improve the network performance. This paper concerns the integrity of routers＇ ACLs in large enterprise networks. We first investigate the integrity of the ACLs of two touters by describing a bottom-up approach for detecting redundancies in ACLs of two routers. We then extend our study to multiple touters and provide a heuristic algorithm for detecting redundant ACLs in multiple touters. We validate the practicality of ouralgorithm through real-life and synthetic router ACL groups of large networks. Performance results show that our heuristic algorithm do not only improve the performance by reducing the number of comparisons overhead, but also helps in discovering potential security holes that can not be discovered by considering the ACLs of each router individually.     

Global core,and galaxy structure of networks

We propose a novel approach,namely local reduction of networks,to extract the global core(GC,for short)from a complex network.The algorithm is built based on the small community phenomenon of networks.The global cores found by our local reduction from some classical graphs and benchmarks convince us that the global core of a network is intuitively the supporting graph of the network,which is"similar to"the original graph,that the global core is small and essential to the global properties of the network,and that the global core,together with the small communities gives rise to a clear picture of the structure of the network,that is,the galaxy structure of networks.We implement the local reduction to extract the global cores for a series of real networks,and execute a number of experiments to analyze the roles of the global cores for various real networks.For each of the real networks,our experiments show that the found global core is small,that the global core is similar to the original network in the sense that it follows the power law degree distribution with power exponent close to that of the original network,that the global core is sensitive to errors for both cascading failure and physical attack models,in the sense that a small number of random errors in the global core may cause a major failure of the whole network,and that the global core is a good approximate solution to the r-radius center problem,leading to a galaxy structure of the network.     

Overlapping community detection combining content and link

In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.     

基于联合矩阵分解的动态异质网络社区发现方法

动态网络的社区发现是目前复杂网络分析领域的重要研究内容,然而现有动态网络社区发现方法主要针对同质网络,当网络包含多种异质信息时,现有方法不再适用。针对这个问题,本文提出了一个基于联合矩阵分解的动态异质网络社区发现方法,首先计算动态异质网路中各个快照图的拓扑相似度矩阵和多关系相似度矩阵,其次利用时序联合非负矩阵分解方法,约束各个时刻快照图的社区划分,最后在真实网络数据集上的实验结果表明,该算法可以有效检测出动态异质网络中潜在的社区结构。     

A novel parallel community detection scheme based on label propagation

Community detection is one of the most important ways to reflect the structures and mechanisms of a social network. The overlapping communities are more in line with the reality of the social networks. In society, the phenomenon of some members sharing memberships among different communities reflects as overlapping communities in the networks. Dealing with big data networks, it is a challenging and computationally complex problem to detect overlapping communities. In this paper, we propose highly scalable variants of a community-detection algorithm in a parallel manner called Label Propagation with nodes Confidence (PLPAC). We introduce MapReduce into our scheme to process the big data in a parallel manner and guarantee the efficiency of community detection. We implemented the algorithm on artificial networks as well as real networks to evaluate the accuracy and speedup of the proposed method. Experimental results on datasets from different scenarios illustrate that the improved label propagation method outperforms the state-of-the-art methods in terms of accuracy and time efficiency.     

结合拓扑势与信任度调整的重叠社区发现算法

现实世界中的复杂系统可建模为复杂网络,探究复杂网络中的社区发现算法对于分析复杂网络的拓扑结构和层次结构具有重要作用。早期研究通常将网络中的节点局限在一个社区中,但随着研究的深入发现社区结构呈现重叠特性。针对现有重叠社区发现算法存在划分社区结构不稳定、忽略节点交互和属性等问题,提出一种基于网络拓扑势与信任度调整的重叠社区发现算法。融合节点的属性和结构特征计算节点的拓扑势,依据节点的拓扑势选取核心节点。从核心节点出发构建初始社区群,计算各个社区间的调整信任度,实现社区的合并与再调整,从而识别重叠社区。在多个人工模拟网络和真实网络数据集上的实验结果表明,与基于贪婪派系扩张、种子扩张等的重叠社区发现算法相比,该算法将扩展模块度最高提升至0.719,能有效识别社区结构及重叠节点,提升重叠社区检测性能。     

模块度增量与局部模块度引导下的社区发现算法

社区结构是复杂网络的重要特性之一,基于层次聚类的社区发现算法很好地利用了模块度来挖掘网络中的社区结构,但其局限性也导致算法对社区结构复杂的网络划分不够准确、无法发现小于一定规模的社区。在层次聚类的基础上,提出引入局部模块度来弥补模块度在划分社区时的不足,避免可能出现的划分不合理情况。通过真实数据集和人工网络进行了验证,实验结果证明,该算法具有可行性与有效性。     

基于节点依赖度和相似社团融合的社团结构发现算法

社团结构作为复杂网络的拓扑特性之一具有重要的理论和实践意义。提出一种基于节点依赖度和相似社团融合的社团结构发现算法,首先根据依赖度和相似度的定义将整个网络划分成若干个平均集聚系数较大的局部网络,构成网络的基础骨架社团;然后根据连接度的定义不断将社团边缘的节点和小社团吸收到相应的骨架网络中去,直到所有节点都得到准确的社团划分。算法在Zachary空手道俱乐部网络和海豚社会网络中进行了社团划分实验,并与GN算法和Newman快速算法进行了比较,结果表明该算法可以有效地划分社团边缘的模糊节点,社团划分结果具有较高的准确度。     

Finding overlapping communities in a complex network of social linkages and Internet of things

Online social networks play an important role in today’s Internet. These social networks contain huge amounts of data and the integrated framework of SN with Internet of things (IoT) presents a challenging problem. IoT is the ubiquitous interconnection of everyday items of interest (things), providing connectivity anytime, anywhere, and with anything. Like biological, co-authorship, and virus-spread networks, IoT and Social Network (SN) can be characterized to be complex networks containing substantial useful information. In the past few years, community detection in graphs has been an active area of research (Lee and Won in Proceedings of IEEE SoutheastCon, pp. 1–5, 2012). Many graph mining algorithms have been proposed, but none of them can help in capturing an important dimension of SNs, which is friendship. A friend circle expands with the help of mutual friends, and, thus, mutual friends play an important role in social networks’ growth. We propose two graph clustering algorithms: one for undirected graphs such as Facebook and Google+, and the other for directed graphs such as Twitter. The algorithms extract communities, and based on the access control policy nodes share resources (things). In the proposed Community Detection in Integrated IoT and SN (CDIISN) algorithm, we divide the nodes/actors of complex networks into basic, and IoT nodes. We, then, execute the community detection algorithm on them. We take nodes of a graph as members of a SN, and edges depicting the relations between the nodes. The CDIISN algorithm is purely deterministic, and no fuzzy communities are formed. It is known that one community detection algorithm is not suitable for all types of networks. For different network structures, different algorithms exhibit different results, and methods of execution. However, in our proposed method, the community detection algorithm can be modified as desired by a user based on the network connections. The proposed community detection approach is unique in the sense that a user can define his community detection criteria based on the kind of network.     

一种基于粗糙集和密度峰值的重叠社区发现方法

随着互联网和社会的发展,各个领域每天都会产生大量相互关联、彼此依赖的数据,这些数据根据不同的主题形成了各种复杂网络。挖掘社区结构是复杂网络领域中的一项重要研究内容,因为其在推荐系统、行为预测和信息传播等方面具有极其重要的意义。社区结构中的重叠社区结构在生活中普遍存在,更具有实际研究意义。为有效发现复杂网络中的重叠社区,文中引入了粗糙集理论对社区进行分析,识别出重叠节点,进而提出了一种基于粗糙集和密度峰值的重叠社区发现方法OCDRD(Overlapping Community Detection Algorithm Based on Rough Sets and Density Peaks)。该方法在传统网络节点局部相似性度量的基础上,结合灰色关联分析方法求出网络节点间的全局相似性,进而将其转化为节点间距离。将密度峰值聚类算法的思想应用于该算法中,以根据网络结构自动选取社区中心节点。依据网络中节点的距离比例关系,定义了社区的上近似、下近似以及边界域。最后,不断调整距离比率阈值并进行划分迭代,在每次迭代中针对社区的边界域进行计算,从而获得最佳重叠社区划分结构。在LFR基准人工网络数据集和真实网络数据集上,基于标准互信息(Normalized Mutual Information,NMI)和具有重叠性模块度EQ这两个评价指标,将OCDRD方法与近几年效果较好的其他社区发现算法进行测试比较。实验结果显示,OCDRD方法在社区划分结构方面整体优于其他社区发现算法,表明了该算法的可行性和有效性。     

A hybrid artificial immune network for detecting communities in complex networks

One of the challenging problems when studying complex networks is the detection of sub-structures, called communities. Network communities emerge as dense parts, while they may have a few relationships to each other. Indeed, communities are latent among a mass of nodes and edges in a sparse network. This characteristic makes the community detection process more difficult. Among community detection approaches, modularity maximization has attracted much attention in recent years. In this paper, modularity density (D value) has been employed to discover real community structures. Due to the inadequacy of previous mathematical models in finding the correct number of communities, this paper first formulates a mixed integer non-linear program to detect communities without any need of prior knowledge about their number. Moreover, the mathematical models often suffer from NP-Hardness. In order to overcome this limitation, a new hybrid artificial immune network (HAIN) has been proposed in this paper. HAIN aims to use a network’s properties in an efficient way. To do so, this algorithm employs major components of the pure artificial immune network, hybridized with a well-known heuristic, to provide a powerful and parallel search mechanism. The combination of cloning and affinity maturation components, a strong local search routine, and the presence of network suppression and diversity are the main components. The experimental results on artificial and real-world complex networks illustrate that the proposed community detection algorithm provides a useful paradigm for robustly discovering community structures.     

Incremental community miner for dynamic networks

Human relationships have led to complex communication networks among different individuals in a society. As the nature of relationship is change, these networks will change over the time too which makes them dynamic networks including several consecutive snapshots. Nowadays, the pervasiveness of electronic communication networks, so called Social Networks, has facilitated obtaining this valuable communication information and highlighted as one of the most interesting researchers in the field of data mining, called social network mining. One of the most challenging issues in the field of social network mining is community detection. It means to detect hidden communities in a social network based on the available information. This study proposes an appropriate solution to find and track communities in a dynamic social network based on the local information. Our approach tries to detect communities by finding initial kernels and maintaining them in the next snapshots. Using well-known datasets, the investigation and comparison of the proposed method with some state-of-the-art approaches indicates that the performance and computation complexity of our method is promising and can outperform its competitors.     

一种基于增量式谱聚类的动态社区自适应发现算法

针对当前复杂网络动态社区发现的热点问题, 提出一种面向静态网络社区发现的链接相关线性谱聚类算法, 并在此基础上提出一种基于增量式谱聚类的动态社区自适应发现算法. 动态社区发现算法引入归一化图形拉普拉斯矩阵呈现复杂网络节点之间的关 系,采用拉普拉斯本征映射将节点投影到k维欧式空间.为解决离群节点影响谱聚类的效果和启发式确定复杂网络社区数量的问题, 利用提出的链接相关线性谱聚类算法发现初始时间片的社区结构, 使发现社区的过程能够以较低的时间开销自适应地挖掘复杂网络社区结构. 此后, 对于后续相邻的时间片, 提出的增量式谱聚类算法以前一时间片聚类获得的社区特征为基础, 通过调整链接相关线性谱聚类算法实现对后一时间片的增量聚类, 以达到自适应地发现复杂网络动态社区的目的. 在多个数据集的实验表明, 提出的链接相关线性谱聚类算法能够有效地检测出复杂网络中的社区结构以及基于 增量式谱聚类的动态社区自适应发现算法能够有效地挖掘网络中动态社区的演化过程.