基于LDA的结构-内容联合社团发现模型

在社会网络中,根据已有的连接关系和文本信息发掘社会网络中的社团不但可以将相似的用户划分在一个社团,还可以用来预测网络中潜在的连接关系。为了提高社会网络中社团发现的性能,本文提出了一种基于LDA的结构-内容联合社团发现模型。首先,对社会网络的图论描述进行转化,使其适用于LDA模型。其次,对LDA模型描述进行扩充,使其包含了用户间交互的文本信息。最后,通过Gibbs采样方法对模型的参数进行估计。实验表明,本文提出的社团发现模型与其它相关方法相比较,社团发现得到的社团不仅用户间连接的紧密度和用户共享兴趣爱好的强度高,而且可以更好地用于社会网络中潜在连接的预测。     

A framework for joint community detection across multiple related networks

Community detection in networks is an active area of research with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. With the rapid proliferation of online social media, it has become increasingly common for web users to have noticeable presence across multiple web sites. This raises the question whether it is possible to combine information from several networks to improve community detection. In this paper, we present a framework that identifies communities simultaneously across different networks and learns the correspondences between them. The framework is applicable to networks generated from multiple web sites as well as to those derived from heterogeneous nodes of the same web site. It also allows the incorporation of prior information about the potential relationships between the communities in different networks. Extensive experiments have been performed on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous community detection over three alternative methods, including normalized cut and matrix factorization on a single network or a bipartite graph.     

群智感知网络中基于社会关系的社区发现算法

群智感知应用主要通过社区划分进行任务分配,然而现有群智感知应用中社区发现算法缺乏对社会关系的量化以及划分社区的特征因子单一。针对这些问题,提出了一种基于多维社会关系特征的社区发现算法,通过计算移动节点间的最优生成树、节点合并因子、社区调整因子,对移动节点的社会关系进行具体量化,将节点合理划分成不同的社区。实验结果表明,与现有方法相比,该算法在不同的数据集中具有更好的动态适应性、有效性和预测准确性,其准确性平均达到97.3%,高于对比算法15.2%。     

Finding Communities by Decomposing and Embedding Heterogeneous Information Network

Community discovery is an important task in social network analysis.However,most existing methods for community discovery rely on the topological structure alone.These methods ignore the rich information available in the content data.In order to solve this issue,in this paper,we present a community discovery method based on heterogeneous information network decomposition and embedding.Unlike traditional methods,our method takes into account topology,node content and edge content,which can supply abundant evidence for community discovery.First,an embedding-based similarity evaluation method is proposed,which decomposes the heterogeneous information network into several subnetworks,and extracts their potential deep representation to evaluate the similarities between nodes.Second,a bottom-up community discovery algorithm is proposed.Via leader nodes selection,initial community generation,and community expansion,communities can be found more efficiently.Third,some incremental maintenance strategies for the changes of networks are proposed.We conduct experimental studies based on three real-world social networks.Experiments demonstrate the effectiveness and the efficiency of our proposed method.Compared with the traditional methods,our method improves normalized mutual information(NMI)and the modularity by an average of 12%and 37%respectively.     

Who to follow recommendation in large-scale online development communities

ContextOpen source development allows a large number of people to reuse and contribute source code to the community. Social networking features open opportunities for information discovery, social collaborations, and improved recommendations of potential collaborators.ObjectiveOnline community and development platforms rely on social network features to increase awareness and attention among community members for improved collaborations. The objective of this work is to introduce an approach for recommending relevant users to follow. Follower networks provide means for informal information propagation. The efficiency and effectiveness of such information flows is impacted by the network structure. Here, we aim to understand the resilience of networks against random or strategic node removal.MethodSocial network features of online software development communities present a new opportunity to enhance online collaboration. Our approach is based on the automatic analysis of user behavior and network structure. The proposed ‘who to follow’ recommendation algorithm can be parametrized for specific contexts. Link-analysis techniques such as PageRank/HITS provide the basis for a novel ‘who to follow’ recommendation model.ResultsWe tested the approach using a GitHub-based dataset. Currently, users follow popular community members to get updates regarding their activities instead of maintaining personal relations. Thus, social network features require further improvements to increase reciprocity. The application of our ‘who to follow’ recommendation model using the GitHub dataset shows excellent results with respect to context-sensitive following recommendations. The sensitivity of GitHub’s follower network to random node removal is comparable with other social networks but more sensitive to follower authority based node removal.ConclusionLink-based algorithm can be used for context-sensitive ‘who to follow’ recommendations. GitHub is highly sensitive to authority based node removal. Information flow established through follower relations will be strongly impacted if many authorities are removed from the network. This underpins the importance of ‘central’ users and the validity of focusing the ‘who to follow’ recommendations on those users.     

基于ICN网络架构的社区感知型MSN路由机制

移动社交网络（mobile social network，简称MSN）利用移动用户之间的社交关系，通过节点间的协作式转发实现消息交付.然而，随着大数据时代的到来，MSN需要满足移动用户日益增长的对内容（如视频）的需求.由于信息中心网络（information-centric networking，简称ICN）对移动性的支持，基于ICN架构，提出了一种MSN中基于社区划分的路由机制.在兴趣决策中，利用节点请求中的内容名字获取用户的兴趣偏好，进而计算用户间的兴趣差异度量；根据兴趣差异将节点划分为兴趣社区，依据这些兴趣社区进行兴趣包路由.在数据决策中，根据节点历史相遇信息计算用户间的相遇规律度量，根据相遇规律将节点划分为社交社区，依据这些社交社区进行数据包路由.同时，根据兴趣社区和社交社区信息优化节点的内容缓存，以快速满足未来的内容请求.进行了仿真实验，通过与现有机制在包交付率、平均延迟、平均跳数和网络开销方面的性能对比，表明所提出的机制是可行且有效的.     

Discovering and tracking query oriented active online social groups in dynamic information network

The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies.

     

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.     

Like-minded communities: bringing the familiarity and similarity together

Community detection in social networks is a well-studied problem. A community in social network is commonly defined as a group of people whose interactions within the group are more than outside the group. It is believed that people’s behavior can be linked to the behavior of their social neighborhood. While shared characteristics of communities have been used to validate the communities found, to the best of authors’ knowledge, it is not demonstrated in the literature that communities found using social interaction data are like-minded, i.e., they behave similarly in terms of their interest in items (e.g., movie, products). In this paper, we experimentally demonstrate, on a social networking movie rating dataset, that people who are interested in an item are socially better connected than the overall graph. Motivated by this fact, we propose a method for finding communities wherein like-mindedness is an explicit objective. We find small tight groups with many shared interests using a frequent item set mining approach and use these as building blocks for the core of these like-minded communities. We show that these communities have higher similarity in their interests compared to communities found using only the interaction information. We also compare our method against a baseline where the weight of edges are defined based on similarity in interests between nodes and show that our approach achieves far higher level of like-mindedness amongst the communities compared to this baseline as well.     

社交网Twitter平台的人物关系网社区发现

社交网络服务（social networking service,SNS）已融入到大众生活中。人们将自己的信息上传到网络中,并通过社交网站管理自己的社交圈子,由此造成大量的个人信息在社交网络上被公开。文章基于Twitter平台,设计实现了Twitter用户关系网的社区发现。通过实时采集Twitter用户信息,重建人物关系网,改进Newman快速算法划分社区发现人物关系网。文章通过可视化的界面呈现用户的社区关系,提供用户网络行为,为决策者的舆情监控或个性推荐提供了参考凭据。     

语义增强的大规模多元图简化可视分析方法

网络图可视化可以有效展示网络节点之间的连接关系,广泛应用于诸多领域,如社交网络、知识图谱、生物基因网络等.随着网络数据规模的不断增加,如何简化表达大规模网络图结构已成为图可视化领域中的研究热点.经典的网络图简化可视化方法主要包括图采样、边绑定和图聚类等技术,在减少大量点线交叉造成的视觉紊乱的基础上,提高用户对大规模网络结构的探索和认知效率.然而,上述方法主要侧重于网络图中的拓扑结构,却较少考虑和利用多元图节点的多维属性特征,难以有效提取和表达语义信息,从而无法帮助用户理解大规模多元网络的拓扑结构与多维属性之间的内在关联,为大规模多元图的认知和理解带来困难.因此,本文提出一种语义增强的大规模多元图简化可视分析方法,首先在基于模块度的图聚类算法基础上提取出网络图的层次结构;其次通过多维属性信息熵的计算和比较分析,对网络层次结构进行自适应划分,筛选出具有最优属性聚集特征的社团;进而设计交互便捷的多个关联视图来展示社团之间的拓扑结构、层次关系和属性分布,从不同角度帮助用户分析多维属性在社团形成和网络演化中的作用.大量实验结果表明,本文方法能够有效简化大规模多元图的视觉表达,可以快速分析不同应用领域大规模多元图的关联结构与语义构成,具有较强的实用性.     

基于结构的社会网络分析

互联网的发展和社交网站的流行为研究社会网络提供了大规模的实验平台.主要使用DBLP和Facebook数据集构建网络,采取角色连接轮廓方法从结构上进行划分,发现它们属于外围串类型;验证了社会网络的一些统计性质,比如无标度分布、稠化定律和直径缩减等;发现社会网络中存在紧密连接且直径较小的核心结构,规模中等的社区主要呈现星型结构;基于事件框架研究了社会网络中社区结构的进化,发现社区间的融合很大程度上取决于社区间直接连接的节点所构成网络的聚类系数,而社区的分裂则与该社区的聚类系数相关.     

用于网络重叠社区发现的粗糙谱聚类算法

针对绝大多数社区发现算法都存在着网络节点仅隶属于一个社区的假设,引入谱图理论与粗糙集理论来分析复杂网络社区,提出一种用于网络重叠社区发现的粗糙谱聚类算法RSC,该算法用上下近似来刻画网络节点的社区归属,边界表示社区之间共享的节点,通过优化重叠社区结构模块度来实现重叠社区发现.通过3个不同类型真实网络的仿真实验,结果验证了该方法的可行性与有效性.     

基于链接分析和用户兴趣的微博社区发现算法

微博网络中的每一个节点代表一个微博用户,微博用户之间除了存在一定的社会关系外,用户本身也具有一定的特性。用户之间明显的链接关系可以为社区发现提供重要的线索,但两个用户之间如果有相似的兴趣,那么即使用户之间没有明显的链接关系他们也可能加入到同一个社区,而传统的社区发现方法大多基于单个方面进行考虑。所以,提出一种结合链接分析和用户兴趣的微博社区发现方法。真实数据集上的仿真实验验证了该方法的合理性和有效性。     

基于节点动态属性相似性的社会网络社区推荐算法

社区推荐帮助用户寻找感兴趣的社群,是社会网络分析的重要内容。根据社会网络的动态变化特性,通过分析网络的动态演变过程、网络个体的行为特征及个体间联系的变化,研究动态社区及其个体的动态特性的形式化描述方法,提出了节点（个体）间的动态属性相似度计算方法和基于节点（个体）间的动态属性相似度计算的社区推荐算法,可以克服通过个体的直接联系进行社区推荐的局限性。实验结果表明,应用本算法进行社区推荐的准确率有较大提高,能有效应用于动态社会网络的社区推荐。     

复杂网络大数据中重叠社区检测算法

大数据时代互联网用户数量呈爆炸性增长,社交网络、电商交易网络等复杂网络规模快速发展,准确有效地检测复杂网络大数据中重叠社区结构对用户兴趣点推荐和热点传播具有重要意义。提出一种新的面向复杂网络大数据的重叠社区检测算法DOC（Detecting Overlapping Communities over complex network big data）,时间复杂度为O（nlog²（n））,算法基于模块度聚类和图计算思想应用新的节点和边的更新方法,利用平衡二叉树对模块度增量建立索引,基于模块度最优的思想设计一种新的重叠社区检测算法。相对于传统重叠节点检测算法,对每个节点分析的频率大大降低,可以在较低的算法运行时间下获得较高的识别准确率。复杂网络大数据集上的算法测试结果表明：DOC算法能够有效地检测出网络重叠社区,社区识别准确率较高,在大规模LFR基准数据集上其重叠社区检测标准化互信息指标NMI最高能达到0.97,重叠节点检测指标F-score的平均值在0.91以上,且复杂网络大数据下的运行时间明显优于传统算法。     

基于SimRank全局矩阵平滑收敛的网络社区发现

SimRank方法是一种基于图的拓扑结构信息来衡量任意两个对象间相似程度的方法,针对在真实的大规模社交网络中节点与节点之间的迭代计算过程需要消耗大量的时间,提出了一种基于SimRank全局矩阵平滑收敛的网络社区发现方法（SimRank global smooth convergence,SGSC）。首先,该算法通过经典度量来识别网络中的初始核心节点;然后利用矩阵平滑收敛来计算SimRank得到最终核心节点;最后,基于全局收敛矩阵,将社区聚集在核心节点周围,使用Closeness指数合并两个社区,通过递归的重复该过程,聚类出最终社区。在3种真实的不同规模的社交网络中将SGSC和其他2种具有代表性的方法进行比较,并验证了提出的算法在不同规模的社交网络中社区划分的准确率和算法运行的时间性能上有所提升。     

基于移动网络位置信息的群体发现方法

当前群体发现研究主要利用通联关系挖掘用户群体, 未能充分利用网络中所隐含的用户社交关系, 致使挖掘的群体不能真实反映用户在社会生活中的群体关系。提出一种基于用户位置信息的群体发现方法, 利用序列模式挖掘算法挖掘用户位置规律序列, 建立位置序列相关性度量标准, 以位置规律相关性揭示用户社会活动的群体关系; 结合局部相似性度量方法计算用户通信距离指数, 反映用户之间的相识程度; 最后采用通信距离指数对位置相关性进行加权计算用户群体相关性, 再利用分裂聚类算法挖掘具有通信关系和社交关系的用户群体。实验结果表明, 该方法能够有效地挖掘用户社交活动中的通信相关性和位置相关性, 体现用户在现实社会活动中的群体关系。     

面向社交推荐的自适应高阶隐式关系建模

近年来,社交推荐的研究主要聚焦于社交网络中显式、隐式关系的联合建模,却忽视了高阶隐式关系并非对每个用户都同等重要这一特殊现象.高阶隐式关系对一个有着足够多邻居的用户与一个仅有少量邻居的用户重要性存在明显差异.此外,由于社交关系建立的随机性,显式关系并不总是可用的.提出了一种新的自适应高阶隐式关系建模方法 (adaptive high-order implicit relations modeling, AHIRM),该模型由3个部分组成:首先,过滤不可靠关系且识别出潜在可靠关系.旨在避免不可靠关系带来的负面影响,并部分缓解数据稀疏的问题;其次,设计自适应随机游走算法,结合规范化后的节点中心度为用户捕获不同阶数的邻居,构建用户间的高阶隐式关系,进而重构社交网络;最后,运用图卷积网络(graph convolutional network, GCN)聚合邻居节点信息,更新用户嵌入,实现高阶隐式关系建模,从而进一步缓解数据稀疏问题.在建模过程中,同时考虑到社交结构和个人偏好的影响,模拟并保留了社交影响传播的过程.在LastFM、Douban和Gowalla这3个数据集上与相关算法做了对比验证...     

Discovery of Web user communities and their role in personalization

One of the major innovations in personalization in the last 20?years was the injection of social knowledge into the model of the user. The user is not considered an isolated individual any more, but a member of one or more communities. User communities have been facilitated by the striking advancements of electronic communications and in particular the penetration of the Web into people??s everyday routine. Communities arise in a number of different ways. Social networking tools typically allow users to proactively connect to each other. Alternatively, data mining tools discover communities of connected Web sites or communities of Web users. In this article, we focus on the latter type of community, which is commonly mined from logs of users?? activity on the Web. We recall how this process has been used to model the users?? interests and personalize Web applications. Collaborative filtering and recommendation are the most widely used forms of community-driven personalization. However, we examine a range of other interesting alternatives that are worth investigating further. This effort leads us naturally to the recent developments on the Web and particularly the advent of the social Web. We explain how this development draws together the different viewpoints on Web communities and introduces new opportunities for community-based personalization. In particular, we propose the concept of active user community and show how this relates to recent efforts on mining social networks and social media.