Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Centrality is one of the most important fields of social network research. To date, some centrality measures based on topological features of nodes in social networks have been proposed in which the importance of nodes is investigated from a certain point of view. Such measures are one dimensional and thus not feasible for measuring sociological features of nodes. Given that the main basis of Social Network Analysis (SNA) is related to social issues and interactions, a novel procedure is hereby proposed for developing a new centrality measure, named Sociability Centrality, based on the TOPSIS method and Genetic Algorithm (GA). This new centrality is not only based on topological features of nodes, but also a representation of their psychological and sociological features that is calculable for large size networks (e.g. online social networks) and has high correlation with the nodes' social skill questionnaire scores. Finally, efficiency of the proposed procedure for developing sociability centrality was tested via implementation on the Abrar Dataset. Our results show that this centrality measure outperforms its existing counterparts in terms of representing the social skills of nodes in a social network.     

Looking for trouble: A multilevel analysis of disagreeable contacts in online social networks

Identifying characteristics of troublemakers in online social networks, those contacts who violate norms via disagreeable or unsociable behaviour, is vital for supporting preventative strategies for undesirable, psychologically damaging online interactions. To date characterising troublemakers has relied on self-reports focused on the network holder, largely overlooking the role of network friends. In the present study, information was obtained on 5113 network contacts from 52 UK-based Facebook users (age range 13–45; 75% female) using digitally derived data and in-depth network surveys. Participants rated their contacts in terms of online disagreement, relational closeness and interaction patterns. Characteristics of online troublemakers were explored using binary logistic multilevel analysis. Instances of online disagreement were most apparent in the networks of emerging adults (19–21 years). Contacts were more likely to be identified as online troublemakers if they were well connected within the network. Rates of offline and Facebook exchanges interacted such that contacts known well offline but with low rates of Facebook communication were more likely to be identified as troublemakers. This may indicate that users were harbouring known troublemakers in a bid to preserve offline relationships and reputational status. Implications are discussed in terms of an individual's susceptibility to undesirable encounters online.     

复杂网络中的在线社交网络演化模型

在线社交网络是一种广泛存在的社会网络,其节点度遵循幂率分布规律,但对于其结构演化模型方面的相关研究还不多。基于复杂网络理论研究在线社交网络内部结构特征,提出一种结合内增长、外增长及内部边更替的演化模型,借助平均场理论分析该模型的拓扑特性,实验和理论分析表明由该模型生成的网络,其度分布服从幂率分布,且通过调整参数,幂率指数在1~3,能较好地反映不同类型的真实在线社交网络的度分布特征,因此具有广泛适用性。     

基于Dijkstra算法的社交网络抽样生成

针对社交网络中随机抽样算法抽样结果不能很好地代表原始网络的问题,设计了一种基于Dijkstra最短路径的抽样算法。首先,利用Dijkstra算法多次抽取社交网络中节点之间的最短路径;然后,对抽取到的路径中边出现的频率进行排序,选择较高频率的边组成抽样的子图。该算法解决了随机抽样算法存在的一些问题,实现了较好的生成抽取社交网络的功能。仿真实验结果表明,与随机抽样方法相比,所提抽样算法能减少抽样误差,更好地反映原始网络。     

Hybrid system of ART and RBF neural networks for online clustering

An online clustering task is considered for machine state monitoring purpose. In the previous authors’ researches a classical ART-2 network was tested for online classification of operational states in the context of a wind turbine monitoring. Some drawbacks, however, were found when a data stream size had been increased. This case is investigated in this paper. Classical ART-2 network can cluster data incorrectly when data points are compared by using Euclidean distance. Furthermore, ART-2 network can lose accuracy when data stream is processed for long time. The way of improving the ART-2 network is considered and two main steps of that are taken. At first, the stereographic projection is implemented. At the second step, a new type of hybrid neural system which consists of ART-2 and RBF networks with data processed by using the stereographic projection is introduced. Tests contained elementary scenarios for low-dimensional cases as well as higher dimensional real data from wind turbine monitoring. All the tests implied that an efficient system for online clustering had been found.     

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。     

Efficiency analysis of information technology and online social networks management: An integrated DEA-model assessment

This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business “social Media” activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model's robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company's ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN.     

Automatic assignment of reviewers in an online peer assessment task based on social interactions

Online peer assessment tasks are very popular and have unique characteristics that improve learning and encourage social interactions in a distance education environment. Unfortunately, social factors have usually been ignored in the process of selecting reviewers for online peer assessment tasks. We hypothesise that this fact could have some influence on the lack of engagement and participation by some learners. For this reason, we propose an approach in which social network analysis techniques, expert criteria, and Bayesian reasoning are applied to select reviewers with the objective of increasing participation in peer review tasks. The approach is divided into two elements. On the one hand, we have developed an influence diagram template that structures a set of proposed social network analysis variables in accordance with expert criteria. This influence diagram template can be easily updated for any course simply by eliciting a minimal set of parameters. On the other hand, we have instantiated the proposed influence diagram template to produce an influence diagram network to quantify the quality of reviewer assignment for an online peer assessment task. In an online experiment, we verified that the consideration of social factors can increase participation in a peer assessment task.     

French-DeGroot社会网络模型的结构辨识与参数估计

近年来社会网络的研究受到越来越多的关注.本文研究基于French-DeGroot模型的社会网络参数和结构辨识问题,通过网络中个体所持的观点来判断个体间是否存在影响关系、进一步估计个体之间影响的大小.具体而言:假设网络存在固执个体(stubborn agents)和非固执个体(non-stubborn agents)两类,当固执个体的观点为零均值独立同分布随机变量序列时,利用最小二乘算法估计网络未知参数,证明了估计的强一致性并给出收敛速度;进一步,构造结构辨识算法判断个体间是否存在影响关系,证明了结构辨识算法的有限时间收敛性.最后给出仿真例子验证算法的有效性.     

Automatic analysis and identification of verbal aggression and abusive behaviors for online social games

Online multiplayer games create new social platforms, with their own etiquette, social rules of conduct and ways of expression. What counts as aggressive and abusing behavior may change depending on the platform, but most online gaming companies need to deal with aggressive and abusive players explicitly. This usually is tied to a reporting mechanism where the offended player reports an offense. In this paper, we develop tools for validating whether a verbal aggression offense report refers to a real offense or not, in the context of a very popular online social game, called Okey. Our approach relies on the analysis of player behavior and characteristics of offending players. In the proposed system, chat records and other social activities in the game are taken into account, as well as player history. This methodology is sufficiently generic, and it can be applied to similar gaming platforms, thus describing a useful tool for game companies. We report our results on data collected over a six months period, involving 100,000 users and 800,000 game records, and illustrate the viability of such analysis, while providing insights on the factors associated with verbal aggression and abusive behavior for social games.     

基于聚类分析分库策略的社交网络数据库查询性能与数据迁移

社交网络数据具有一定的聚合性,即特征上相近的用户之间更容易产生某种行为。依照常规的水平切分方法,在执行这些事件的信息查询时,将会耗费大量的时间和连接损耗去依次访问多个数据库。针对此问题,提出了基于聚类分析的社交网络数据库分库策略。将社交网络主体的特征标量进行聚类,使得聚集程度高的主体尽量分割到一个或尽可能少的几个分库中去,从而提高事件的查询效率,并在此基础上兼顾负载均衡与大数据迁移等问题。实验结果表明,该策略在社交网络的主流事件查询上都表现出不同程度的性能提升,最高提升程度达到23.4%,并且实现了局部最优负载均衡和零数据迁移。总的来说,基于聚类分析的社交网络数据库分库策略在提高查询效率、平衡负载以及大数据迁移可行性上,比传统水平切割分库有了相当的优势。     

基于改进蚁群优化算法的网络服务质量路由算法

针对当前无线网络路由算法存在丢包率高、节点拥塞严重的难题,提出一种基于改进蚁群优化算法的网络服务质量路由算法。首先根据无线网络的特点选择带宽、端到端的延迟、数据包丢失率以及链路花费作为QoS参数,并建立一个多约束网络服务质量路由优化问题的数学模型,然后采用具有正反馈机制和搜索能力强的蚁群优化算法对数学模型进行求解,并根据无线网络路由特点对标准蚁群优化算法进行改进,提高其搜索性能,最后采用具体仿真实验对路由算法的性能进行测试。实验结果表明,改进蚁群优化算法在满足网络质量要求的条件下,不仅降低了网络平均延时,而且减少了网络数据丢包率,性能优于其它算法。     

基于Spark GraphX和社交网络大数据的用户影响力分析

利用社交网络大数据进行用户影响力分析,有助于识别网络环境中影响力强的用户以实现其社会和商业价值。传统方法无法高效处理海量社交网络数据,定量准确地分析用户影响力。为解决该问题,本文提出一种基于PageRank算法的改进的用户影响力评价模型,综合考虑了用户连接程度和活跃程度,并以支持大规模并行图计算的Spark GraphX为工具,快速高效地实现了微博用户影响力的定量分析与评价。实验结果表明,本文所提方法效率更高,得到的用户影响力结果更接近真实情况。     

基于遗传算法与人工神经网络相结合的固体推进剂燃速计算

为了提高固体推进剂燃速预示精度,将遗传算法(Genetic Algorithm)与误差反传(Back Propagation)网络结构模型相结合,设计了用遗传算法优化神经网络权重的新方法。以固体推进剂燃速数据库为基础,对推进剂的燃速进行了预估,并与BP算法进行了比较。结果显示,预估值与实际值接近,误差小于BP算法模型,具有良好的预示效果,为推进剂燃速预估提供了新方法。