Assessment of subjective influence and trust with an online social network game

The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.     

在线社会网络演化模型

在分析在线社会网络的拓扑结构、特征及演化规律的基础上,借鉴了前人网络模型的思想,提出了在线社会网络演化模型,引入动态的加权方式,提出了一种在线社会网络演化模型。理论分析和仿真表明：在线社会网络演化模型具有无标度和小世界特性,点权、边权、度分布呈现幂律特性,具有较多的簇系数、较小的路径长度且可调。这种无标度和小世界特性与现实中的在线社会网络较为一致。     

基于社会网络的信任模型研究

研究了社会网络的原理以及现有信任模型的研究现状,并在此基础上结合网络交易和社会网络的特点,考虑了时间带来的信任衰减、交易风险等级、关系紧密程度等因素,建立了基于社会网络的互联网信任模型,通过计算结点间的信任值来衡量信任程度,仿真实验表明该模型能有效地识别出自夸点,能够防止通过小额交易积累信任值,之后进行大额交易欺骗的行为。仿真结果表明,该模型具有可行性。     

基于Mapreduce的大规模社会网络提取方法研究*

从海量非规范Web数据源提取大规模高质量的社会网络有着广阔应用前景和较高学术价值,同时也面临着海量计算所带来的巨大挑战。为此,以Digg新闻评论网站为信息源,以提取网站用户之间的共同兴趣网络为主要目标,提出了基于云平台的社会网络提取系统框架,实现了基于Mapreduce的大规模社会网络提取方法。实验结果表明,提出的方法具有较好的扩展性和伸缩性,能够胜任从异构Web数据源提取高质量的大规模社会网络的计算任务。     

基于熵权TOPSIS的社会网络影响力最大化研究

在影响力最大化问题中,针对启发式方法精度不足和贪婪方法时间过载的问题,提出一种基于多属性决策方法的影响力最大化算法.首先,从社会网络节点的影响传播、节点之间的影响重叠和节点的信任度角度选取节点的重要性评价指标.然后,建立基于熵权TOPSIS的社会网络节点重要性评价模型,通过模型选择影响范围最广、与当前种子集的重叠最小且信任度最高的节点.最后,构建算法,并通过实验验证算法的性能.实验结果表明,与传统影响力最大化算法相比,所提算法在传播范围与时间效率上取得了较好的折中.     

在线社交网络中延伸访问控制机制研究

针对社交网络中用户发布的数据延伸不可控的问题,提出了一种基于隐私标签的延伸控制机制。该机制基于用户关系跳数和资源转发跳数给用户和数据分配不同类型的隐私标签,以实现对数据的细粒度延伸访问控制。提出了隐私标签的生成算法和分配方法,设计了隐私标签约束规则并对可能出现的策略冲突进行分析。最后通过测试,表明了该机制可以实现社交网络中细粒度延伸控制,同时证明了该机制的安全性和有效性。     

DeepMUI: A novel method to identify malicious users on online social network platforms

The use of online social network (OSN) platforms has become an essential component of contemporary society, facilitating global connectivity, and information sharing among individuals. The proliferation of malicious users has emerged as a noteworthy obstacle, exerting a detrimental effect on the authenticity of the data disseminated through these channels. A malicious profile is created with the intention of disseminating false information, manipulating perspectives, and executing harmful actions, including phishing schemes, identity theft, and the propagation of malware. Consequently, the identification of malicious users has emerged as an essential undertaking for both OSN platforms and researchers. The objective of this study is to investigate the issue of identifying malicious users on OSN platforms. The DeepMUI model has been introduced as a new approach to identifying malicious users on OSN platforms, utilizing user profile metadata-derived characteristics. The DeepMUI architecture is composed of long short-term memory and convolutional neural network models. Additionally, it integrates alterations to the pooling layer to improve its overall efficacy. The experiments have demonstrated that DeepMUI exhibits promising results in the task of identifying malicious users, with greater accuracy and minimal loss compared to existing methods.     

社会语义网分析技术研究

为满足在线社会网络语义分析的需要,提出社会语义网络分析框架。该框架由两部分构成:一是在线社会网络的语义表示,利用RDF模型和已建立的本体描述在线社会网络,赋予社会网络丰富的语义信息;二是在线社会网络的语义分析,利用SPARQL对在线社会网络语义图进行检索过滤,获取满足语义要求的数据,在分析过程中利用属性的层次结构实现分析粒度的控制,通过属性路径检索实现整体网分析。通过应用案例,说明了所提框架的有效性。     

Motivators of online vulnerability: The impact of social network site use and FOMO

Continued and frequent use of social network sites (SNS) has been linked to a fear of missing out (FOMO) and online self-promotion in the form of friending and information disclosure. The present paper reports findings from 506 UK based Facebook users (53% male) who responded to an extensive online survey about their SNS behaviours and online vulnerability. Structural equation modelling (SEM) suggests that FOMO mediates the relationship between increased SNS use and decreased self-esteem. Self-promoting SNS behaviours provide more complex mediated associations. Longitudinal support (N = 175) is provided for the notion that decreased self-esteem might motivate a potentially detrimental cycle of FOMO-inspired online SNS use. The research considers the implications of social networking on an individual's online vulnerability.     

动态社会网络隐私保护方法研究*

针对动态社会网络数据多重发布中用户的隐私信息泄露问题,结合攻击者基于背景知识的结构化攻击,提出了一种动态社会网络隐私保护方法。该方法首先在每次发布时采用k-同构算法把原始图有效划分为k个同构子图,并最小化匿名成本;然后对节点ID泛化,阻止节点增加或删除时攻击者结合多重发布间的关联识别用户的隐私信息。通过数据集实验证实,提出的方法有较高的匿名质量和较低的信息损失,能有效保护动态社会网络中用户的隐私。     

The social influence of online collaborative community: the moderating effect of achievement

Online collaborative communities become particularly influential in contemporary Internet economy. However, these communities are often characterised by limited liability. Following the perspective of social influence, this study examines the impacts of three social influence modes in online collaborative communities of a famous online game. The moderating role of a player’s game achievement is also explored. Our results show that community identification is the most influential on online game continuance intention, especially for high achievement players. Community value congruence is likely to affect online game continuance intention for low achievement players. The impact of community normative influence on online game continuance intention appears to be curvilinear. This is more obvious for low achievement players. Implications for research and practice are also discussed.     

Interpersonal influence on online game choices

Several theories stress the importance of interpersonal influence on an individual’s adoption of a product or service. However, there has been little research that empirically examined how online friends influence an individual’s online product choices. This study examines the effect of a game player’s online friends who adopted a game earlier than the player on the likelihood that the player adopts the game. Two main factors considered in this study are: (1) the number of online friends who adopted a game earlier and (2) the strength of ties between the player and the player’s online friends who adopted the game earlier. Using a hazard model with data on 1,668 game players’ gaming activities and relational connections, we find (1) the likelihood that a player adopts a particular game increases the more her online friends adopted the game earlier, and (2) the influence of the prior adopter friends on the likelihood that a player adopts the game varies with the strength of ties between the player and her prior adopter friends. But the p-values of the coefficients for the corresponding independent variables are larger than the conventional cutoff point, 0.05. Possible causes for this statistical insignificance are discussed in the text.     

在线社会网络的k-shell结构研究

分析研究了Twitter与You Tube两个在线社会网络的结构。用k-shell(k-壳)分解法对网络分解,并对比分析了它们的入(出)度、入(出)k-shell、以及度与k-shell之间的关系,发现它们之间有较大的差异。You Tube的入(出)度、入(出)k-shell分布均服从幂律分布,而Twitter的分布服从漂移幂律分布、指数截断的幂律分布,但它们的度与k-shell关系基本相同,都未表现出较强的相关性。此外,根据度相关系数的定义还提出k-shell相关性的定义及其计算方法,并用来刻画网络k-shell之间的同(异)配性。     

在线社会网络的网络结构和信息传播研究综述

随着在线社会网络的大规模应用和普及, 亟需对在线社会网络进行深入研究分析。在线社会网络的网络结构和信息传播研究是该领域中的两大研究热点和关键问题。网络结构包括关键节点、网络关系以及社团的挖掘, 通过对网络结构的分析可以掌握被分析网络中存在的社团、节点之间的关系以及关键节点等, 而这种分析对于国家及时掌握在线社会网络的舆情、公司广告在网络上投放策略的制定都具有极大的帮助。对在线社会网络信息传播的研究主要有信息传播动力模型、信息传播源和路径的发现与描绘、信息传播的最大化和最小化等, 通过对在线社会网络信息传播的研究, 人们可以对在线社会网络信息传播的影响进行预测和干预, 从而可以将信息传播的影响按照有利的方向引导。综述了在线社会网络的网络结构和信息传播的研究现状, 并对这两方面的主要研究方法及技术的优势和不足以及适用场合进行了对比分析。     

弱连接对不同类型在线社交网络信息传播范围的影响研究

按照连接强度的不同,在线社交网络节点间的连接可以分为强连接和弱连接,可以通过网络上两个节点的邻居相对重叠来测量连接强度。实验表明,弱连接对于信息传播范围的影响与具体的网络类型有关系,在基于信息交换的在线社交网络中,例如移动电话通信网络、Wiki投票网络,移去弱连接并不会对信息收敛时传播的范围产生明显的影响;而在基于合作关系形成的在线社交网络中,例如Youtube、Facebook、CDBLP合作网,移去弱连接对信息传播的范围有明显的阻碍作用。     

基于k-核过滤的社交网络影响最大化算法

针对现有社交网络影响最大化算法影响范围小和时间复杂度高的问题,提出一种基于独立级联模型的k-核过滤算法。首先,介绍了一种节点影响力排名不依赖于整个网络的现有影响力最大化算法;然后,通过预训练k,找到对现有算法具有最佳优化效果且与选择种子数无关的k值;最后,通过计算图的k-核过滤不属于k-核子图的节点和边,在k-核子图上执行现有影响最大化算法,达到降低计算复杂度的目的。为验证k-核过滤算法对不同算法有不同的优化效果,在不同规模数据集上进行了实验。结果显示,应用k-核过滤算法后：与原PMIA算法相比,影响范围最多扩大13.89%,执行时间最多缩短8.34%;与原核覆盖算法（CCA）相比,影响范围没有太大差异,但执行时间最多缩短28.5%;与OutDegree算法相比,影响范围最多扩大21.81%,执行时间最多缩短26.96%;与Random算法相比,影响范围最多扩大71.99%,执行时间最多缩短24.21%。进一步提出了一种新的影响最大化算法GIMS,它比PMIA和IRIE的影响范围更大,执行时间保持在秒级别,而且GIMS算法的k-核过滤算法与原GIMS算法的影响范围和执行时间差异不大。实验结果表明,k-核过滤算法能够增大现有算法选择种子节点集合的影响范围,并且减少执行时间;GIMS算法具有更好的影响范围效果和执行效率,并且更加鲁棒。     

The impact of social network embeddedness on mobile massively multiplayer online games play

Social factors play a critical role in motivating player participation and commitment to online multiplayer games. Many popular mobile massively multiplayer online games (MMOGs) adopt social network embeddedness (SNE) functions to optimise players' social play experience. SNE changes the traditional pattern of MMOG social play by porting acquaintance relationships (e.g., Facebook friends) from social networking sites to the virtual game world. However, little understanding exists on how SNE impacts mobile MMOG players' game participation results such as play performance and play frequency. Drawing on the affordance framework and social capital literature, this research proposes a theoretical model that integrates the factors of SNE technology affordance (identity transparency and information transparency), players' social experience (social interaction, social support, shared vision, and social pressure), plus affordance effects (play performance and play frequency). The model was validated through a longitudinal field study, in which both subjective and objective data were collected from Game for Peace players. Our findings indicate that identity transparency and information transparency positively correlate with social interaction, social support, shared vision, and social pressure, which, taken together, significantly affect play frequency. The results also show that social interaction and shared vision positively impact players' play performance. The study enhances the theoretical understanding of social relationships in players' game participation results from the SNE aspect. Finally, we lend insights on how game operators can improve player game experience and stickiness.     

An empirical test to measure the effectiveness of online advertising in online marketplaces using a hierarchical Bayes model

Online marketplace, taken the form of “open market” where a very large number of buyers and sellers participate, has occupied a rapid increasing position in e-commerce, which resulting in sellers’ increasing investment on online advertising. Hence, there is a growing need to identify the effectiveness of online advertising in the online marketplaces such as eBay.com. However, it is problematic to directly apply the existing online advertising effect models for click-through data of online marketplaces. Therefore, there is a need for developing a model to estimate the effectiveness of online advertising in online marketplace considering its characteristics. In this paper, we develop an analytical Bayesian approach to modeling click-though data by employing the Poisson-gamma distribution. Our results have implications for online advertising effect measurement, and may help guide advertisers in decision-making.     

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。     

Flocking based evolutionary computation strategy for measuring centrality of online social networks

Centrality in social network is one of the major research topics in social network analysis. Even though there are more than half a dozen methods to find centrality of a node, each of these methods has some drawbacks in one aspect or the other. This paper analyses different centrality calculation methods and proposes a new swarm based method named Flocking Based Centrality for Social network (FBCS). This new computation technique makes use of parameters that are more realistic and practical in online social networks. The interactions between nodes play a significant role in determining the centrality of node. The new method has been calculated both empirically as well as experimentally. The new method is tested, verified and validated for different sets of random networks and benchmark datasets. The method has been correlated with other state of the art centrality measures. The new centrality measure is found to be realistic and suits well with online social networks. The proposed method can be used in applications such as finding the most prestigious node and for discovering the node which can influence maximum number of users in an online social network. FBCS centrality has higher Kendall’s tau correlation when compared with other state of the art centrality methods. The robustness of the FBCS centrality is found to be better than other centrality measures.