在线社交网络社区的启发式挖掘框架

指出基于全局优化的社区挖掘方法的不足,给出OSNs网络及其社区挖掘的形式定义,提出一个启发式社区挖掘框架,在此框架下对包括LWP,Clauset,Schaeffer,Papadopoulos,Bagrow与Chen在内的6种启发式社区挖掘算法进行分析比较.通过3个真实OSNs网络的实验比较,验证了启发式社区挖掘框架的可行性,在结果社区有效性与时间效率上对6种启发式算法进行比较,实验结论为网络社区挖掘的工程实践与理论研究提供了借鉴.     

Exploring the power of social hub services

World Wide Web - Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory...     

Dynamic evolution of collective emotions in social networks: a case study of Sina weibo

Emotion is a fundamental object of human existence and determined by a complex set of factors. With the rapid development of online social networks (OSNs), more and more people would like to express their emotion in OSNs, which provides wonderful opportunities to gain insight into how and why individual emotion is evolved in social network. In this paper, we focus on emotion dynamics in OSNs, and try to recognize the evolving process of collective emotions. As a basis of this research, we first construct a corpus and build an emotion classifier based on Bayes theory, and some effective strategies (entropy and salience) are introduced to improve the performance of our classifier, with which we can classify any Chinese tweet into a particular emotion with an accuracy as high as 82%. By analyzing the collective emotions in our sample networks in detail, we get some interesting findings, including a phenomenon of emotion synchronization between friends in OSNs, which offers good evidence for that human emotion can be spread from one person to another. Furthermore, we find that the number of friends has strong correlation with individual emotion. Based on those useful findings, we present a dynamic evolution model of collective emotions, in which both self-evolving process and mutual-evolving process are considered. To this end, extensive simulations on both real and artificial networks have been done to estimate the parameters of our emotion dynamic model, and we find that mutual-evolution plays a more important role than self-evolution in the distribution of collective emotions. As an application of our emotion dynamic model, we design an efficient strategy to control the collective emotions of the whole network by selecting seed users according to k-core rather than degree.     

Persönlichkeit 2.0

Online Social Networks (OSNs) wie MySpace, Facebook und StudiVZ sind inzwischen zu einem wichtigen Bestandteil des allt?glichen Miteinanders geworden. Entgegen der weit verbreiteten Meinung vermitteln Profile in OSNs kein verf?lschtes, selbstidealisiertes, sondern ein sehr genaues Bild von der Pers?nlichkeit der Profilbesitzer. Dies k?nnte ein wichtiger Grund für die Beliebtheit sozialer Netzwerke sein: Sie bieten eine Plattform für reale soziale Interaktionen und erm?glichen den Ausdruck der eigenen Pers?nlichkeit. Implikationen für den Datenschutz in OSNs werden diskutiert.     

基于时间距离与拓扑距离的连通性模型

网络连通性是描述网络性能的一项重要指标.机会传感网络中,节点移动导致网络的拓扑呈动态变化,这使得机会传感网络连通性的表征面临挑战.建立机会传感网络的连通性模型,有助于对机会传感网络的优化和维护.针对机会传感网络拓扑频繁变化的特点,基于时空图理论构建其连通性模型,描述拓扑演化规律;根据消息的可达性,考虑消息传输的时间特性和空间特性,定义了时间距离和拓扑距离,采用统计产品与服务解决方案软件（SPSS）分析其相关性,结果表明,时间距离和拓扑距离无明显相关性;采用网络快照间的时间距离与拓扑距离构建整网连通性模型.实验结果表明,所提出的模型能够从整体上刻画出机会传感网络的连通性;与基于Katz中心性的网络连通度模型相比,该模型能够更好地反映整网连通度的变化.     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

Information propagation in online social networks: a tie-strength perspective

In this paper, we investigate the relationship between the tie strength and information propagation in online social networks (OSNs). Specifically, we propose a novel information diffusion model to simulate the information propagation in OSNs. Empirical studies through this model on various real-world online social network data sets reveal three interesting findings. First, it is the adoption of the information pushing mechanism that greatly facilitates the information propagation in OSNs. Second, some global but cost-intensive strategies, such as selecting the ties of higher betweenness centralities for information propagation, no longer have significant advantages. Third, the random selection strategy is more efficient than selecting the strong ties for information propagation in OSNs. Along this line, we provide further explanations by categorizing weak ties into positive and negative ones and reveal the special bridge effect of positive weak ties. The inverse quantitative relationship between weak ties and network clustering coefficients is also carefully studied, which finally gives reasonable explanations to the above findings. Finally, we give some business suggestions for the cost-efficient and secured information propagation in online social networks.     

Concentration tuning mediated by spare receptor capacity in olfactory sensory neurons: A theoretical study.

The olfactory system is capable of detecting odorants at very low concentrations. Physiological experiments have demonstrated odorant sensitivities down to the picomolar range in preparations from the sensory epithelium. However, the contemporary model for olfactory signal transduction provides that odorants bind to olfactory receptors with relatively low specificity and consequently low affinity, making this detection of low-concentration odorants theoretically difficult to understand. We employ a computational model to demonstrate how olfactory sensory neuron (OSN) sensitivity can be tuned by modulation of receptor-effector coupling and/or by other mechanisms regulating spare receptor capacity, thus resolving this conundrum. The EC10-90 intensity tuning ranges (ITRs) of whole olfactory glomeruli and postsynaptic mitral cells are considerably broader than the commensurate ITRs of individual OSNs. These data are difficult to reconcile with certain contemporary hypotheses that convergent OSNs in mammals exhibit a homogeneous population of olfactory receptors and identical tuning for odor stimuli. We show that heterogeneity in spare receptor capacities within a convergent OSN population can increase the ITR (EC10-90) of a convergent population of OSNs regardless of the presence or absence of a diversity of receptor expression within the population. The modulation of receptor-effector coupling has been observed in OSNs; other mechanisms for cellular regulation of spare receptor capacity are also highly plausible (e.g., quantitative regulation of the relative expression levels of receptor and effector proteins). We present a model illustrating that these processes can underlie both how OSNs come to exhibit high sensitivity to odorant stimuli without necessitating increased ligand-receptor binding affinities or specificities and how a population of convergent OSNs could exhibit a broader concentration sensitivity than its individual constituent neurons, even given a population expressing identical odorant receptors. The regulation of spare receptor capacity may play an important role in the olfactory system's ability to reliably detect low odor concentrations, discriminate odor intensities, and segregate this intensity information from representations of odor quality.     

Design and analysis of a social botnet

Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today’s web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8 weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80%, (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today’s underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security.     

Querying dynamic communities in online social networks简

Online social networks （OSNs） offer people the opportunity to join communities where they share a common interest or objective. This kind of community is useful for studying the human behavior, diffusion of information, and dynamics of groups. As the members of a community are always changing, an efficient solution is needed to query information in real time. This paper introduces the Follow Model to present the basic relationship between users in OSNs, and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying. Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system. Based on 75 GB message data and 26 GB relation network data from Twitter, a case study was realized using two dynamic discussion communities：#musicmonday and #beatcancer. The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.     

Content delivery simulations supported by social network-awareness

In this study we conduct experiments on a modified content delivery simulation framework, as we aspire to compare miscellaneous policies for dynamic OSN-aware content delivery. The incorporation of an OSN-aware dynamic mechanism becomes indispensable for CDN services, since (i)significantly large proportion of Internet traffic results from -easily produced via online media services and transmitted over OSNs- bandwidth-intensive multimedia content and (ii)multimedia content providers, such as YouTube, often rely on ubiquitous Content Distribution Networks (CDNs) infrastructures. Our policies take patterns of user activity over OSNs and exploit geo-social properties of users participating in retransmissions of items over OSNs (social cascades), proceed to incorporate various caching schemes of the underlying infrastructure, different policies for the handling of OSN data and various approaches that take into account the efficient timing of prefetching. The simulation framework we introduce can serve as the basis of further parameterized content delivery experimentation that exploits information transmission over OSNs and decreases replication costs by selectively copying items to locations where items are likely to be consumed.     

Content delivery simulations supported by social network-awareness

In this study we conduct experiments on a modified content delivery simulation framework, as we aspire to compare miscellaneous policies for dynamic OSN-aware content delivery. The incorporation of an OSN-aware dynamic mechanism becomes indispensable for CDN services, since (i)significantly large proportion of Internet traffic results from -easily produced via online media services and transmitted over OSNs- bandwidth-intensive multimedia content and (ii)multimedia content providers, such as YouTube, often rely on ubiquitous Content Distribution Networks (CDNs) infrastructures. Our policies take patterns of user activity over OSNs and exploit geo-social properties of users participating in retransmissions of items over OSNs (social cascades), proceed to incorporate various caching schemes of the underlying infrastructure, different policies for the handling of OSN data and various approaches that take into account the efficient timing of prefetching. The simulation framework we introduce can serve as the basis of further parameterized content delivery experimentation that exploits information transmission over OSNs and decreases replication costs by selectively copying items to locations where items are likely to be consumed.     

Distance-based customer detection in fake follower markets

As the Online Social Networks (OSNs) have become popular, more and more people want to increase their influence not only in the real world but also in the OSNs. However, increasing the influence in OSNs is time-consuming job, so some users want to find a shortcut to increase their relationships. The demand for quick increasement of relationship has led to the growth of the fake follower markets that cater to customers who want to grow their relationships rapidly. However, customers of fake follower markets cannot manipulate legitimate user’s relationship perfectly. Existing approaches explore node’s relationships or features to detect customers. But none of them combines the relationships and node’s features directly. In this article, we propose a model that directly combines the relationship and node’s feature to detect customers of fake followers. Specifically, we study the geographical distance for 1-hop-directional links using the nodes geographical location. Motivated by the difference of a distance ratio for 1-hop directional links, the proposed method is designed to generate a 1-hop link distance ratio, and classifies a node as a customer or not. Experimental results on a Twitter dataset demonstrate that the proposed method achieves higher performance than other baseline methods.     

A Cost-Efficient Approach to Storing Users’ Data for Online Social Networks

Journal of Computer Science and Technology - As users increasingly befriend others and interact online via their social media accounts, online social networks (OSNs) are expanding rapidly....     

微博谣言事件自动检测研究

互联网大数据环境下,谣言事件的散播已成为以微博为代表的在线社交网络持续健康稳定发展的主要障碍之一,因此及时有效地进行谣言事件自动检测对营造清朗的网络环境和维护社会和谐发展有着现实意义。该文以微博事件为背景,综合谣言事件特征随时间变化特性以及时间维度上谣言事件的分布特点,引入论域划分思想,基于模糊聚类算法提出了随时间动态变化的事件时序特征构建模型;同时,基于社会学中谣言的传播原理,提出将事件流行度、模糊度和流传度作为微博谣言事件检测分类器的三项新特征。实验结果表明,该文提出的动态时序特征表示方法和三项新特征使谣言事件自动检测效果得到了可观提升。     

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.     

Query-oriented topical influential users detection for top-k trending topics in twitter

Applied Intelligence - Online Social Networks (OSNs) have become inevitable for any new methodology both for viral promoting applications and instructing the creation of inciting information and...     

User profiling for big social media data using standing ovation model

Multimedia Tools and Applications - Online Social Networks (OSNs) have recently been the subject of numerous studies that have attempted to develop effective methods for classifying and analyzing...     

Privacy protection of online social network users,against attribute inference attacks,through the use of a set of exhaustive rules

Neural Computing and Applications - A malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By...     

Online Social Network Adoption: A Cross-Cultural Study

This research explores the extent to which users across cultures adopt the technology of online social networks (OSNs) in order to promote or support a social cause. By surveying graduate-level university students at institutions in the United States, China and India, this research builds on prior work in technology acceptance to model and explain how three elements of the task domain – the cultural aspects of the user, the social nature of the technology, and the social nature of the task – combine to influence the constructs and relationships within a modified Technology Acceptance Model (TAM) framework as well as the behavior that flows from it. This study contributes to our understanding of technology adoption by showing how OSNs are adopted by users across cultures in promoting and supporting social causes.