网络社区发现优化：基于随机游走的边权预处理方法

在网络日趋复杂化、巨大化的背景下,仅依靠网络拓扑特征难以提高现有社区发现算法的精确度和性能。该文提出一种优化网络社区发现的边权预处理方法,基于马尔可夫随机游走理论建模社区结构对复杂网络行为的影响,根据多重随机游走对网络连接的遍历情况,重新衡量网络边权。预处理后的边权作为网络拓扑的有效补充信息,能够将网络社区结构去模糊化,从而改善现有算法的社区发现性能。对于一些典型的计算机生成网络和真实网络,经实验验证：该预处理方法能够有效提升现有部分社区发现算法的准确性和效率。     

基于节点相似度的CCN社区划分方法

针对内容中心网络(Content Centric Networking,CCN)中路由检索过程造成大量低效的冗余问题,进行了更深一层的研究,结合节点相似度、标签传播等方法,将CCN网络拓扑划分为多个社区。该方法首先将CCN网络中各个请求节点以及节点内容进行名字解析,以便获取用户的兴趣偏好,并将节点中影响力较高的节点作为社区核心节点,再将节点划分社区,实现内容快速缓存,有效地避免了CCN网络检索所造成的数据冗余问题,进一步提高整个网络速度,提高资源利用率,减少数据冗余。     

电信行业社区营销

电信行业社区营销是指根据电信企业的需要,按照地域、区域、用户的消费习惯、消氪县次等对现有通信用户划分社区,电信企业按照划分的用户社区建立相应的营销服务管理体系,按照用户社区开展营销活动,为用户提供通信服务。     

电信行业社区营销

电信行业社区营销是指根据电信企业的需要,按照地域、区域、用户的消费习惯、消费层次等对现有通信用户划分社区,电信企业按照划分的用户社区建立相应的营销服务管理体系,按照用户社区开展营销活动,为用户提供通信服务.     

基于图熵聚类的重叠社区发现算法

图聚类算法是数据挖掘和复杂网络研究中的一个关键环节。基于密度、层次划分的方法已经被广泛应用于流行病学、新陈代谢和科学引文写作中。尽管上述的聚类方法适用于复杂网络的社区发现,但精度受到限制,其中一个最大的挑战是重叠社区的生成。为填补这一缺口,提出了一种利用图熵搜索局部最优的聚类方法。与传统的基于密度的种子生长式方法不同,在每一次迭代中,引入图熵来衡量图结构的模块度,并为种子的选择提供了随机选择、基于节点的度和基于节点的聚类系数3种方案。经过自下而上迭代的聚类,引入准确率和召回率等评价指标评估聚类结果的精确度,证明了算法的有效性。     

基于影响力与种子扩展的重叠社区发现

社区发现作为复杂社交网络中一个重要的研究方向.针对目前基于种子节点的算法在种子选取与扩展等方面的不足,提出了一种基于影响力与种子扩展的重叠社区发现算法（Influence Seeds Extension Overlapping Community Detection,简称i-SEOCD算法）.首先,利用节点影响力策略找出具有紧密结构的种子社区.其次,从这些种子社区出发,计算社区邻居集节点与社区的相似度,并取出相似度超过设定阈值的节点.然后,采用优化自适应函数的策略来扩展社区.最后,对网络中的自由节点进行社区隶属划分,进而实现了整个网络的重叠社区结构挖掘.在真实社交网络和人工生成网络上实验表明,i-SEOCD算法能够准确、快速地发现复杂网络中的重叠社区结构.     

基于微博媒体的社区发现技术研究

微博社交网络是由节点构成的,每个节点代表一个微博用户。节点与节点间存在着关系,因此连接紧密的节点形成了社区。如何从微博社交网络中挖掘出社区,已成为Web2．0的团体挖掘研究热点。详细介绍了传统的网络团体挖掘算法,并提出了一种新的社区发现的算法,称为基于用户兴趣的社区发现算法。该算法不论在计算效率还是社区发现效果上比传统算法都具有明显的提升,取得了不错的实验效果。     

社会网络中基于局部信息的边社区挖掘

 近年来,随着社交网络的发展,许多重叠社区挖掘算法被提出来.传统的方法都是将节点作为研究对象,而最近的一些研究表明,以边为研究对象的边社区挖掘方法相对于点社区挖掘方法来说具有更加明显的优势.因此,我们提出了基于局部边社区的挖掘算法(LLCM),利用网络中的局部信息去挖掘边社区结构.给定一条初始的边,通过不断最大化一个适应度函数来获取该边所在的局部社区,而这条初始的边可以预先通过一些排序算法进行选择.算法经过在计算机生成网络和真实网络上测试,并且同其他边社区挖掘算法进行了比较,实验结果表明LLCM算法获取了合理的边社区的结构.     

基于复杂网络的社区划分算法分析

近年来,复杂网咯吸引了大量的学者,作为一个新兴起来的学科,来自各个领域的学者们都开始对其进行研究分析。社区划分是复杂网络的重要特征之一。针对复杂网络中社区划分问题,对三种的社区划分算法进行了研究,传统GN算法、FN算法和谱聚类算法,分别阐述了各种算法的基本原理,并对这两种算法基于真实世界网络模型进行了适当的分析和比较,选取出较为高效的谱聚类算法,用于现实世界复杂网络中的社区划分,为实际应用中社区划分算法的认识与应用提供了方法参考。     

基于核心标签的可重叠微博网络社区划分方法

针对传统微博社区发现算法内聚低重叠度不可控制等问题,以自顶向下的策略,提出一种基于核心标签的可重叠微博社区发现策略Tag Cut.先利用用户标签的共现关系及逆用户频率对标签进行加权,并基于标签之间的内联及外联关系并将用户的标签进行扩充,然后在整体社区中提取包含某一标签的用户作为临时分组并利用评价函数评估划分的优劣,最后选出最合适的核心标签根据其对应分组与其他分组距离的远近来决定将其划分为新的分组还是并入其他分组.用此策略反复迭代直到满足要求.该算法划分的组由若干个拥有核心标签的分组组成且综合利用微博用户已声明的及隐含的兴趣、用户之间的关注规律、结果的实用性对划分结果进行修正.经真实数据实验表明该方法内聚高社区重叠度可控且拥有实际意义.     

Virus infection control in online social networks based on probabilistic communities

With the rapid progress of the Internet, more and more people socialize and make new friends through online social network sites and applications, such as Facebook, Twitter, and MSN Messenger. The number of users of these online social network sites and applications has increased significantly within a short period. Unfortunately, online social networks are often the platforms that propagate malicious software, such as viruses. The malicious software is spread by infected users who automatically send fake requests to other users. After accepting the fake requests, the users are infected. In a realistic environment, everyone can decide whether to accept or decline a request, and thus, we study how to construct and use community structures to efficiently control virus propagation in online social networks whose users each have a probability, namely, inclination, of accepting a request. In this paper, a community detection algorithm is proposed to detect communities in online social networks. In addition, a number of users are selected from the communities for patching and controlling virus propagation. Simulation results show that our proposed method provides good performance in terms of the number of distributed patches.Copyright © 2013 John Wiley & Sons, Ltd.     

SoLoMo在音乐领域的应用

音乐+社交的模式最近几个月在国外也涌现了一些发展良好的音乐社区,但音乐+SoLoMo的应用到目前为止还没有见到。中国电信爱音乐的"哎姆DJ音乐电台"(简称哎姆DJ)则是音乐+SoLoMo的创新应用。哎姆DJ支持Android、iOS(iPhone、iPod、iPad)和Web访问。除了常规在线电台的收听音乐功能,还可以与在线的听友留言聊天,还能获知听友与自己的距离;"音乐缘分"功能显示与你最有缘的听友……可以说哎姆DJ具备了完整的SoLoMo要素。本文结合国内外业界的最新发展情况,介绍了"哎姆DJ"的主要特点、关键技术和运营状况,并展望了SoLoMo在音乐领域的前景。     

Research on the scalability of parallel community detection algorithms

The social network often contains a large amount of information about users and groups,such as topic evolution mode,group aggregation effect,the law of information dissemination and so on.The mining of these information has become an important task for social network analysis.As one characteristic of the social network,the group aggregation effect is characterized by the community structure of the social network.The discovery of community structure has become the basis and key point of other social network analysis tasks.With the rapid growth of the number of online social network users,the traditional community detection methods have been difficult to be used,which contributes to the development of parallel community detection technology.The current mainstream parallel community detection methods,including Louvain algorithm and label propagation algorithm,were tested in the large-scale data sets,and corresponding advantages and disadvantages were pointed out so as to provide useful information for later applications.     

基于异质网络的意见领袖社区发现

针对意见领袖社区发现问题,通过将论坛中主题及其回复关系建模为异质网络,准确表示社区结构.提出意见领袖社区影响力概念及其量化方法,在此基础上设计了一种基于异质网络的意见领袖社区发现算法.通过采集天涯论坛的大量数据,验证了该社区挖掘方案能够较准确地挖掘论坛中的意见领袖社区.     

社交网络中社区领袖的挖掘算法

社交网络中用户和用户之间通过关注而产生联系形成社区。因此,文中借鉴PageRank算法,将传统上把影响力平均分配给关注的人的做法加以改进,依据用户间的亲密程度将影响力按比例分配给关注的人,从而生成新的UserRank算法。算法经过多次迭代计算后,社区中每个用户的影响力收敛并趋于稳定,影响力值最大的用户,就是社区领袖。实验表明,本算法能更快更有效地挖掘出社区领袖。     

Compressive spectrum sensing in the cognitive radio networks by exploiting the sparsity of active radios

Spectrum sensing is a key technology to detect spectrum holes in cognitive network. It has been demonstrated that collaboration among cognitive users can improve the probability of detecting the primary users, but the fusion center is the bottleneck when a lot of collaborative information is transmitted. In this paper, we consider the cognitive radio users only transmit part of sensing information to relieve the transmission load. Besides, the sensing information will be inevitably influenced by various noise in the process of transmission. Therefore, the challenge is how we can detect spectrum holes successfully from these incomplete and inexact measurements. Most recently, there are some research results on this but the detection performance is not satisfactory. In this paper, we firstly formulate the collaborative spectrum sensing as an optimization model and then present a novel adaptive orthogonal matching pursuit algorithm by exploiting the sparsity of active primary users. Statistical property of the sensing data plays a crucial role in spectrum sensing. Theoretical analysis shows the presented scheme can detect active primary users rapidly and efficiently. Simulation results verify that the proposed method can obtain better detection performance with stronger noise background, which is more attractive in real applications.     

Concept Lattices for Knowledge Management

The aim of the method presented in this paper is to support the acquisition of new knowledge and to enhance the interactions between knowledge workers. The approach chosen is to facilitate the sharing of those retrieval terms, which members of a community of practice have used to retrieve valuable information. The nature of information-seeking behaviour in on-line information sources is discussed and then the theory of formal concept analysis is introduced. It is subsequently shown how this theory can be applied to analyse the relations between documents and the retrieval terms that people use to access these documents.The result is a concept lattice that contains information on a community's information-seeking behaviour. The concept lattice uncovers relational and contextual information. Retrieval phrases are put into relational context depending on how they are associated by the documents that are of interest to a community of users. The contention made here is that such 'usage-based' structures will provide natural and intuitive access to information sources for communities of users. It is shown how this approach can be used to facilitate the sharing of the retrieval vocabulary to support the acquisition of new knowledge and to enhance the interactions within a community of practice.     

互联网流量识别中的基于标签传播的重叠社团发现算法

The increased capacity and availability of the Internet has led to a wide variety of applications. Internet traffic characterization and application i-dentification is important for network management. In this paper, based on detailed flow data collected from the public networks of Internet Service Pro-viders, we construct a flow graph to model the in-teractions among users. Considering traffic from different applications, we analyze the community structure of the flow graph in terms of community size, degree distribution of the community, commu-nity overlap, and overlap modularity. The near line-ar time community detection algorithm in complex networks, the Label Propagation Algorithm (LPA), is extended to the flow graph for application identi-fication. We propose a new initialization and label propagation and update scheme. Experimental re-sults show that the proposed algorithm has high ac-curacy and efficiency.     

模块度优化启发式算法应用

模块度优化的启发式快速算法常常用来检测复杂网络中的社团结构.较之其余的社团检测方法,该算法在计算时间上更具优势,而且用模块度衡量发现检测社团的质量很高.运用模块度优化启发式算法划分空手道俱乐部网络、大学足球俱乐部网络和区域贸易网络等,并对其结构和功能做出一定的分析.特别地,针对贸易网络中自由贸易区往往表现为一个社团的特点,以221个国家或地区为研究对象,对贸易协定与地域之间的关系做了大量的实证研究.首先,从世贸组织网站上采集了区域贸易协定中国家之间贸易的数据;其次,通过模块度启发式算法进行社团划分,共得出7个主要的贸易区,其中欧盟自由贸易区的社团表现极为明显;最后,从社团结构的表现形式推断实际区域间的贸易情况.