边缘覆盖去重的社交网络影响力最大化算法

影响力最大化问题是在社交网络中寻找具有最大影响范围的节点集。针对启发式算法准确度相对较差的问题,现有的研究考虑了影响范围重合,但忽略了边缘贡献导致的节点影响力过量评估。重点研究了在考虑边缘贡献的情况下,如何选取影响范围最大的节点集合。采用启发式算法的思想,首先计算节点全局和邻近影响力来评估节点信息传播影响力,通过去除已选节点影响范围并更新网络的方式,消除边缘贡献对节点影响力评估的干扰,在独立级联模型基础上提出了基于边缘去重的节点影响力最大化算法。仿真结果表明所提出算法相比其他算法,能够有效增大节点信息传播影响范围。     

基于PageRank的社交网络影响最大化传播模型与算法研究

社交网络中影响最大化问题是指找出最具有影响力的k个节点,使得最终社交网络中被影响的节点最多,信息传播范围最大。针对影响最大化问题,目前已存在一些基本传播模型,但是这些模型没有考虑网络中节点的相关性和重要性,而网络中节点的相关性和重要性是衡量其影响力的一个重要指标,因此,提出了一种基于网页排名算法的信息传播模型(PageRank-based Propagation Model,PRP),然后利用贪心算法来近似求解影响最大化问题。实验结果表明,基于PageRank的传播模型解决影响最大化问题的效果比传统的线性阈值模型、加权级联模型和独立级联模型的效果更好,影响力范围更大。     

基于3-layer中心度的社交网络影响力最大化算法

社交网络影响最大化问题是指如何寻找网络中有限的初始节点,使得影响的传播范围最广。一些贪心算法可以得到较好的影响范围,但是因时间复杂度太高而不适用于大型社交网络。基于度中心性的启发式算法简单但准确度不高;基于介数中心性、接近中心性等全局指标的启发式算法可以较好地识别影响力最大的节点,但计算复杂度也过高。考虑网络节点深层次结构对影响扩散的作用并权衡计算复杂度与准确度,定义了3-layer局部中心度,以计算节点的潜在影响力值。基于线性阈值模型,启发选择一部分种子节点:每一次都选取潜在影响力最大的节点作为种子节点进行激活;运用贪心算法选取剩下的一部分种子节点:每一次都选取具有最大影响增量的节点作为种子节点进行激活。实验表明,该混合算法具有很好的激活范围以及非常低的时间复杂度。     

基于LT模型的个性化关键传播用户挖掘

文中针对社交网络中特定用户展开个性化关键传播用户挖掘研究,目标是在线性阈值传播模型的基础上,挖掘出能够最大程度影响网络中特定用户的节点集合.尽管在社交网络影响最大化问题方面已存在相关工作,但该文工作偏重于针对网络中的特定用户展开,该问题的解决将有助于企业有效的进行个性化产品营销.为此,文中提出一种基于LT模型的个性化关键传播用户挖掘问题的解决框架.首先,在线性阈值模型的基本传播机制下,提出一个随机函数来模拟基于LT模型的个性化关键传播用户挖掘问题的目标函数,该随机函数具有较小方差的理论保证;然后,提出一个有效的求解算法从网络中挖掘针对特定用户的关键传播节点集合,理论证明该算法具有(1-1/e)的近似精度保证.实验使用真实的社交网络数据验证了算法的有效性.     

基于社区与结构熵的异质网络影响力最大化

影响力最大化的目的是在网络中发现能够触发最大数量的剩余节点参与到信息传播过程的一小群节点.目前异质信息网络中影响力最大化的研究通常从网络中抽取同质子图、或基于节点局部结构的元路径进行节点影响力的评估,没有考虑节点的全局特征和网络中高影响力节点间的集群现象给种子集合最终扩散范围造成的影响损失.文中提出了一种基于社区与结构熵的异质信息网络影响力最大化算法,该算法能够有效地从局部和全局两个方面度量节点的影响.首先,通过构建元结构保留节点在网络中的局部结构信息和异质信息度量节点的局部影响;其次,利用节点所属社区在整个网络中的权重占比对节点的全局影响进行度量;最后,综合求出节点的最终影响并选出种子集合.在真实数据集上进行的大量实验结果表明所提算法有较好的有效性和效率.     

基于反向PageRank的影响力最大化算法

针对社会网络上的影响力最大化算法在大规模网络上难以同时满足传播范围、时间效率和空间效率要求的问题,提出一种混合PageRank和度中心性的启发式算法（MPRD）。首先,基于PageRank,引入一种反向PageRank思想来评估节点影响力;然后,结合局部指标度中心性,设计一种混合的指标来评估节点的最终影响力;最后,通过相似性方法去掉影响力重合严重的节点,选出种子节点集。在6个数据集和两种传播模型上进行实验,实验结果表明,所提的MPRD在传播范围上优于现有的启发式算法,在时间效率上比贪心算法快四、五个数量级,在空间效率上优于基于反向抽样的IMM算法。所提的MPRD在处理大规模网络上的影响力最大化问题时能够取得传播范围、时间效率和空间效率的平衡。     

基于LT+模型的社交网络影响力最大化研究

影响力最大化问题的目标是寻找社交网络中一组种子结点集合,在给定的传播模型下,使得这些结点最终传播的影响范围最大。Kempe和Kleinberg提出的贪心算法可以获得很好的影响范围,但是因复杂度太高而并不适用于大型社交网络。Chen和Yuan等人基于线性阈值(LT)模型提出了构造局部有向无环图的启发式算法,但是LT模型只考虑了邻居结点的直接影响力,忽略了结点之间存在的间接影响力。因此,在LT模型的基础上,结合网络中结点之间存在的间接影响力,提出了LT⁺影响力模型,并利用构造局部有向无环图的启发式算法求解LT⁺模型的影响力最大化,称为LT⁺DAG算法。真实数据集上的对比实验表明,LT⁺DAG算法具有更好的影响范围以及较好的可扩展性。     

基于MRLT模型多关系社交网络影响力最大化研究

社交网络影响力最大化问题是基于特定的传播模型,在网络中寻找一组初始传播节点集合,通过其产生最终传播影响范围最大的一种最优化问题。已有的相关研究大多只是针对单关系社交网络,即在社交网络中只存在一种关系。但在现实中,社交网络的用户之间往往存在着多种关系,并且这多种关系共同影响着网络信息传播及其最终影响范围。在线性阈值模型的基础上,结合网络节点间存在的多种关系,提出MRLT传播模型来建模节点间的影响力传播过程,在此基础上提出基于反向可达集的MR-RRset算法,解决了传统影响力最大化问题研究过程中由于使用贪心算法所导致的计算性能较低的问题。最后通过在真实数据集上的实验对比,表明所提方法具有更好的影响力传播范围及较大的计算性能提升。     

社会网络中基于主题的影响力最大化算法

为了解决现有的影响力最大化研究没有充分考虑主题对影响力节点挖掘的影响而导致特定主题下节点集合的影响范围不大这一问题,本文提出了一种社会网络中基于主题的影响力最大化算法TIM。该算法首先根据主题敏感阈值对初始节点集进行预处理,剔除干扰节点,再在新的节点集合上分两个阶段进行节点挖掘。第一阶段挖掘主题权威性大的节点,第二阶段挖掘主题影响增量最大的节点,最后综合两个阶段的节点作为结果集并进行实验验证。实验结果表明,相比其他算法,TIM算法挖掘的节点集合在特定主题下的影响范围更大,时间复杂度更低。     

基于局部传播中心性的影响力最大化算法

针对度中心性等方法选择种子节点时未考虑节点间传播概率及邻居拓扑连接的影响，提出局部传播中心性LPC(Local Propagation Centrality)的概念。为减少贪心算法时间复杂度高且不可扩展的问题，提出一种新的启发式算法IMLPC(Influence Maximization Algorithm based on LPC)。该算法通过计算每个节点的LPC,依次选择影响力最大的节点。实验结果表明，IMLPC的影响范围和运行时间较现有启发式算法相比有显著提升。在不同数据集下，IMLPC影响范围稳定、可扩展性好。     

How to Archive an Event: Reflections on the Social Repository of Ireland

Reflecting on a feasibility study into archiving social media, this article traces how “events” are defined in various domains and contexts, and employs case studies to analyze key relationships between hashtags and events to provide a critical analysis of how archival events can be constructed out of social events. It provides an overview of the archival and curatorial considerations involved in defining and preserving a social media event, and outlines the technologies developed for the process of collecting, annotating, and preserving social media events. Overall, the article endeavors to reveal how pragmatic considerations, computational approaches and curatorial perspectives shape digital archives and historical narratives.     

BBS中信息传播模式的特征分析

通过比较传染病传播机制与信息传播机制,提出BBS中的信息传播机制模型。通过对BBS中帖子数量变化规律的建模,分析了BBS中信息传播模式的特征,并使用实际数据说明BBS中的信息传播模式。实验表明：BBS可以吸引大批的用户参与,但用户只对部分话题感兴趣并参与讨论;绝大多数话题（占94.9%）帖子数的增长率先增加再减小直至为0,而少量话题（占5.1%）帖子数的增长率直接减小至0。这些结论有助于认识BBS的信息传播机制,对控制和管理BBS的信息传播有启发意义。     

Community building and knowledge sharing by individuals with disabilities using social media

The use of social media to share information, enhance learning, and connect with an online community has grown rapidly over the past 10 years. As social media becomes a more common tool in both formal and informal education, it is imperative to understand how it is used by individuals with disabilities. Through a systematic study of the literature, 215 articles on social media used by individuals with disabilities were selected and 29 selected for in-depth thematic analysis. Six major themes were identified: community, cyberbullying, self-esteem, self-determination, access to technology, and accessibility. To confirm these six categories, we expanded our search, yielding an additional 30 articles, for a total 59 articles reviewed in-depth. Interactions between individuals with disabilities within online communities often had the goal of acquiring knowledge or learning new information. A communities of practice theoretical framework is used to discuss interactions among the elements of social media design, learning, and the building of community by individuals with disabilities.     

基于社会认证的网络身份模型

网络实名制的提出,是为了解决网络匿名性所带来的问题,却又面临实名信息泄露的诟病。造成信息泄露的根源在于实名认证依赖于实名信息。基于社会认证的网络身份模型,依赖社会关系进行身份认证,其利用OSN节点的社会关系构建网络身份,在发挥网络监管作用的同时,避免实名信息的泄漏。模型首先在OSN中依据一定策略选择根节点;然后,采用担保方式进行社会认证;最后,在不依赖实名信息的基础上,构建节点的唯一网络身份SANI。SNAI身份含节点的社会认证信息,具有身份认证和行为溯源的功能。     

Specifying the dynamics of social technologies as social microworlds

This paper explores the affordances of social technologies for supporting the construction of a shareable artefact by a group of learners. A qualitative study that captures the use of five different types of social technologies (Facebook, blogs, wikis, Google Documents and Dropbox) in three different classroom settings sheds light on the potentials and challenges of these tools for supporting material exploration, artefact construction and evaluation. Qualitative content analysis of instructors’ field notes, students’ and instructors’ reflections, interviews and focus groups sheds light on the potential of social technologies to transform the activity of learning across a new culture of computational tools. The affordances of social technologies are discussed as well as design principles that need to be followed in these new arenas.     

Cultivating Social Resources on Social Network Sites: Facebook Relationship Maintenance Behaviors and Their Role in Social Capital Processes

This study explores the relationship between perceived bridging social capital and specific Facebook‐enabled communication behaviors using survey data from a sample of U.S. adults (N=614). We explore the role of a specific set of Facebook behaviors that support relationship maintenance and assess the extent to which demographic variables, time on site, total and “actual” Facebook Friends, and this new measure (Facebook Relationship Maintenance Behaviors) predict bridging social capital. Drawing upon scholarship on social capital and relationship maintenance, we discuss the role of social grooming and attention‐signaling activities in shaping perceived access to resources in one's network as measured by bridging social capital.     

An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions

在社会网络的影响的测量在数据采矿社区收到了很多注意。影响最大化指发现尽量利用信息或产品采纳的有影响的用户的过程。在真实设置,在一个社会网络的一个用户的影响能被行动的集合建模(例如,份额,重新鸣叫,注释) 在其出版物以后由网络的另外的用户表现了。就我们的知识而言,在文学的所有建议模型同等地对待这些行动。然而,它是明显的一工具少些比一样的出版的份额影响的一份出版物相似。这建议每个行动有它影响的自己的水平(或重要性) 。在这份报纸,我们建议一个模型(叫的社会基于行动的影响最大化模型, SAIM ) 为在社会网络的影响最大化。在 SAIM,行动没在测量一个个人的影响力量同等地被考虑,并且它由二主要的步组成。在第一步,我们在社会网络计算每个个人的影响力量。这影响力量用 PageRank 从用户行动被计算。在这步的结束,我们得到每个节点被它的影响力量在标记的一个加权的社会网络。在 SAIM 的第二步,我们计算一个新概念说出 influence-BFS 树的使用的有影响的节点的一个最佳的集合。在大规模真实世界、合成的社会网络上进行的实验在计算揭示我们的模型 SAIM 的好表演,在可接受的时间规模,允许信息的最大的传播的有影响的节点的一个最小的集合。     

Perceived social support moderates the relationships between variables in the social cognition model

Posting behaviour on social networking sites (SNS) has become a method enabling unsatisfied users to vent emotions. Based on social cognition theory (SCT), personal outcome expectations and self-efficacy affect posting behaviour for venting emotions on SNS. However, perceived social support (PSS) may alter the relationships within the SCT model. Thus, this study aimed to explore the moderating effect of PSS on the relationships between variables in the SCT model for venting emotions on SNS. In total, 310 unsatisfied customers in Taiwan were investigated, and structural equation modelling was performed to test the hypotheses. The results indicated that personal outcome expectations and self-efficacy were positively associated with posting behaviour which, in turn, increased venting emotions on SNS. Moreover, PSS moderated the relationships between variables in the SCT model.     

Topology-Based Recommendation of Users in Micro-Blogging Communities

Nowadays,more and more users share real-time news and information in micro-blogging communities such as Twitter,Tumblr or Plurk.In these sites,information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows,named followees.With the increasing number of registered users in this kind of sites,finding relevant and reliable sources of information becomes essential.The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation.To address this problem,we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources.Experimental evaluation conducted with a group of users is reported,demonstrating the potential of the approach.     

Behavior‐Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.