微博网络测量研究

随着移动通信和Web技术的不断突破,以微博为代表的在线社会网络在中国广泛发展起来,越来越多的人开始使用微博进行信息分发和舆论传播.为了了解中国微博网络中的拓扑结构特征和用户行为特征等内在信息,对国内最大的微博系统——新浪微博——开展了主动测量,并结合已有的在线社会网络测量结果,对新浪微博的网络拓扑和用户行为特征进行了分析和比较.主要发现包括:1)新浪微博网络具有小世界特性;2)新浪微博网络的入度分布属于幂次分布,而出度分布表现为某种分段幂率函数;3)与类似社会网络相比,新浪微博网络的出入度不具有相关性;4)新浪微博网络属于同配网络;5)新浪微博用户发博时间具有明显的日分布和周分布模式;6)新浪微博用户博文数目分布表现为威布尔分布;7)新浪微博用户博文的转发和评价行为具有很强的相关性,且博文转发概率要高于评价概率.这些测量研究和发现不仅有助于设计出符合中国微博网络结构特征的数学模型和计算模型,也是实现对微博舆论的监测、引导、控制等方面的重要依据和基础.     

基于行为特征分析的微博恶意用户识别

近年来,社交网络数据挖掘作为物理网络空间数据挖掘的一大热点,目前在用户行为分析、兴趣识别、产品推荐等方面都取得了令人可喜的成果。随着社交网络商业契机的到来,出现了很多恶意用户及恶意行为,给数据挖掘的效果产生了极大的影响。基于此,提出基于用户行为特征分析的恶意用户识别方法,该方法引入主成分分析方法对微博网络用户行为数据进行挖掘,对各维度特征的权重进行排序,选取前六维主成分特征可以有效识别恶意用户,主成分特征之间拟合出的新特征也能提升系统的识别性能。实验结果表明,引入的方法对微博用户特征进行了有效的排序,很好地识别出了微博社交网络中的恶意用户,为其他方向的社交网络数据挖掘提供了良好的数据清洗技术。     

一个基于hadoop的并行社交网络挖掘系统

最近几年,以微博为首的社交网络迅猛发展,这些平台上包含了网民对于时事热点的观点,对生活和人际关系的看法等大量有价值的信息和资源。由于微博数据非常庞大又难以获取等困难,如何有效地对社交网络进行数据挖掘,是近两年数据挖掘研究的重点和热点。本工作设计和实现了一个基于Hadoop的并行社交网络挖掘系统,包含了分布式数据库,并行爬虫,并行数据处理和并行数据挖掘算法集,可以有效地获取和分析挖掘海量的社交网络数据,为社团分析,用户行为分析,用户分类,微博分类等工作提供支持。     

The power of comments: fostering social interactions in microblog networks

Today’s ubiquitous online social networks serve multiple purposes, including social communication (Facebook, Renren), and news dissemination (Twitter). But how does a social network’s design define its functionality? Answering this would need social network providers to take a proactive role in defining and guiding user behavior. In this paper, we first take a step to answer this question with a data-driven approach, through measurement and analysis of the Sina Weibo microblogging service. Often compared to Twitter because of its format,Weibo is interesting for our analysis because it serves as a social communication tool and a platform for news dissemination, too. While similar to Twitter in functionality, Weibo provides a distinguishing feature, comments, allowing users to form threaded conversations around a single tweet. Our study focuses on this feature, and how it contributes to interactions and improves social engagement.We use analysis of comment interactions to uncover their role in social interactivity, and use comment graphs to demonstrate the structure of Weibo users interactions. Finally, we present a case study that shows the impact of comments in malicious user detection, a key application on microblogging systems. That is, using properties of comments significantly improves the accuracy in both modeling and detection of malicious users.     

Mining the interests of Chinese microbloggers via keyword extraction

Microblogging provides a new platform for communicating and sharing information among Web users. Users can express opinions and record daily life using microblogs. Microblogs that are posted by users indicate their interests to some extent. We aim to mine user interests via keyword extraction from microblogs. Traditional keyword extraction methods are usually designed for formal documents such as news articles or scientific papers. Messages posted by microblogging users, however, are usually noisy and full of new words, which is a challenge for keyword extraction. In this paper, we combine a translation-based method with a frequency-based method for keyword extraction. In our experiments, we extract keywords for microblog users from the largest microblogging website in China, Sina Weibo. The results show that our method can identify users’ interests accurately and efficiently.     

A tale of two coalitions – marginalising the users while successfully implementing an enterprise resource planning system

Classic analyses of system implementations view user participation as a key element for successful implementation. However, under some conditions, avoiding user participation offers an alternative route to a successful implementation; this is advisable especially when the user network is weak and aligning user needs with the technological capabilities will take too much resource. To illustrate such situation, we analyse how a successful implementation outcome of an enterprise resource planning (ERP) system emerged in a recently established conglomeration of two previously independent universities. The ERP was used to replace several legacy student administration systems for both political and functional reasons. It was deemed successful by both project consultants and the new university's management while the users were marginalised (‘black boxed’) and left to ‘pick up the pieces’ of an incomplete system using traditional methods such as shadow systems and work‐a‐rounds. Using a process approach and an actor–network theory ‘reading’ of related socio‐technical events, we demonstrate how three networks of actors – management, the project team and the administrative users – collided and influenced the implementation outcome and how the management and project network established the ERP as a reliable ally while at the same time the users – while being enrolled in the network – were betrayed through marginalisation. Our analysis also suggests a useful way to conduct a ‘follow the network’ analysis explaining and accounting for the observed implementation outcome. We illustrate the benefits of using a socio‐technical processual analysis and show how stable actor networks must be constructed during large‐scale information technology change and how different actor groups perceive and influence differently the implementation outcome.     

个性化微博推荐算法

微博不同于传统的社会网络和电子商务网站,存在用户活跃程度低,微博数据稀疏和用户兴趣动态变化等特点,将传统推荐算法应用于微博推荐时,效果并不理想。提出了一种基于贝叶斯个性化排序的微博推荐算法,对用户进行个性化微博推荐。该基于贝叶斯个性化排序的微博推荐算法,以微博对的形式提取微博系统中的隐式信息,对这些微博对进行学习,从而得到用户对不同微博的兴趣值。根据每条微博发出的时间,估计每条微博对的可信度。发出时间越接近的微博对,它的可信度就越高,并且对用户的兴趣值影响就越大。在新浪微博的真实数据上进行实验和评测,结果表明该基于贝叶斯个性化排序的微博推荐算法相比于对比算法,在进行微博推荐时有更好的效果。     

用户关系特征对微博转发行为影响分析研究

转发是微博提供的一个信息传播的机制,用户能够将关注者发布的有趣微博转发到自身平台,然后分享给追随者,是微博网络中信息传播最重要的功能。对于微博网络存在的不同类型连接关系,首先提取出相关特征,如同质性、微网络结构、地理距离以及用户性别等,用于识别连接关系的不同类型,然后采用Log-linear模型来拟合各个特征间系数,基于这些系数对微博用户转发行为形成的内在原因进行了分析。     

基于PageRank的微博用户影响力算法研究

随着Web技术的发展,微博已经成为最受欢迎的社交平台之一了。在中国,微博用户规模已经达到了2.42亿。微博用户影响力计算对社会信息在微博里面有效传播,正确传播,健康传播有着非常重要的意义。本文以新浪微博数据为实验的对象,通过改进传统的PageRank模型,提出了的新型用户影响力排名算法---MBUI-Rank(Micro-Blog User Influence Rank)算法。在考虑传统PageRank方法的用户链接关系的同时,MBUI-Rank算法还考虑到微博用户自身行为活动,构建用户对微博的影响的动态挖掘模型。实验结果表明,MBUI-Rank算法与传统的PageRank算法相比,可以更加真实有效地反映微博用户的实际影响力。     

Towards automating overlay network management

Overlay networks are becoming widely used for content delivery because they provide effective and reliable services that are not otherwise available. However, they can negatively affect each other as well as the underlying network. A management system that controls and adapts their behavior is therefore needed. This will meet not only the specific demands of the users but also those of the network and service providers. This paper presents a novel approach to the issue of automating overlay network management. In contrast to existing management approaches which require static a priori policy configurations, policies are created dynamically. A policy layer consists of a set of policy enforcement points and policy decision points. This is used to capture the goals of users, services, and networks into network-level objectives. The behavior of the overlay network is adapted to the changing conditions in its environment. The creation, adaptation, and termination of overlays are achieved through policies. Policies are generated and enforced on the fly from the context information of the user, the network and the service provider. The new approach provides users and applications with more flexibility to dynamically change their quality-of-service requirements while maintaining smooth quality-of-service delivery. We show the advantages of our architecture and provide simulation results to verify its effectiveness.     

Web-Traveler Policies for Images on Social Networks

Images are one of the most popular type of contents shared on these sites. One of the most popular types of contents shared on these sites is image. Most of these networks offer some rudimentary forms of access controls such as allowing the users to choose who can view their profiles or the images uploaded by them. These controls however apply only in the perimeter of the users’ direct control such as desktops, profiles etc. Users have no control over their content once it is downloaded by others. In order to enable a user to truly maintain control over his content, new access control mechanisms must be designed so as to enable users to control their content even when managed by others. Towards fulfilling this gap, in this paper we propose the concept of “web-traveler policies”. Web-traveler policies allow the user to specify who can view, upload or download a given image within a social network. The unique, innovative feature of web-traveler policies is that they travel with the image, as long as it is hosted on the given social networking site. Additionally, we explore the possibility of extending these controls across different sites, by using the concept of mashups. In the paper we prove the feasibility of this approach, by implementing a working prototype of our approach on a real open source social network platform. We test the performance and scalability of such architecture under heavy user loads and also its resilience towards naïve attacks. This work is a first innovative step toward scalable systems for providing each user with a centralized system for his/her own content’s protection all over the web.     

Cryptanalysis of an optimized protocol for mobile network authentication and security

Yi et al. presented a protocol for mobile network authentication and key distribution based upon the DSA signature scheme. The protocol can be divided into two phases, i.e., registration phase and service phase. In this paper, we show that their scheme is insecure on the registration phase since an attacker can easily forge a certification for any exit or dummy user in their protocol and then pretend to be a legal user to communicate with other entities.     

基于微博的用户社区网络挖掘分析

以新浪微博为研究平台,随机获取微博用户数据作为研究样本,通过共链关系构建社会网络,利用聚类分析方法对样本进行微博关注好友的网络群体分析,网络内部子结构分析和个体角色分析。进而从微博用户好友数据中挖掘关注对象的特征和关注对象间的关联特征,并对改进微博用户关注好友的推荐和信息推送提出一些建议。     

Who is Who on Twitter–Spammer,Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time

Social networks once being an innoxious platform for sharing pictures and thoughts among a small online community of friends has now transformed into a powerful tool of information, activism, mobilization, and sometimes abuse. Detecting true identity of social network users is an essential step for building social media an efficient channel of communication. This paper targets the microblogging service, Twitter, as the social network of choice for investigation. It has been observed that dissipation of pornographic content and promotion of followers market are actively operational on Twitter. This clearly indicates loopholes in the Twitter’s spam detection techniques. Through this work, five types of spammers-sole spammers, pornographic users, followers market merchants, fake, and compromised profiles have been identified. For the detection purpose, data of around 1 Lakh Twitter users with their 20 million tweets has been collected. Users have been classified based on trust, user and content based features using machine learning techniques such as Bayes Net, Logistic Regression, J48, Random Forest, and AdaBoostM1. The experimental results show that Random Forest classifier is able to predict spammers with an accuracy of 92.1%. Based on these initial classification results, a novel system for real-time streaming of users for spam detection has been developed. We envision that such a system should provide an indication to Twitter users about the identity of users in real-time.     

社交网络用户影响力的模糊综合评价

社交网络平台信息传播迅速,为了有效地进行舆情预警,定量地评估用户在消息传播网络中重要性,将模糊综合评价方法引入用户影响力建模问题中. 通过对用户在社交平台上的行为分析,构造了包含用户活跃粉丝数以及平均转发数等五项指标在内的评价体系. 并针对传统的模糊综合评价算法在应用于计算评价指标权重方面的缺陷与不足,提出改进模糊合成算子的方法构建用户影响力评估模型. 利用新浪微博社交平台上的真实数据,结合对比实验和实际评估,改进的模糊合成算子能根据需求调整权重对评价结果的影响,同时该方法能较准确地反应社交网络中用户的实际影响力.     

On Bandwidth-Efficient Overlay Multicast

In this paper, we propose a new multicast delivery mechanism for bandwidth-demanding applications in IP networks. Our mechanism, referred to as multiple-destination overlay multicast (MOM), combines the advantages of IP multicast and overlay multicast. We formulate the MOM routing problem as an optimization problem. We then design an algorithm based on Lagrangian relaxation on our formulation and propose a distributed protocol based on the algorithm. For network operators, MOM consumes less network bandwidth than both IP multicast and overlay multicast. For users, MOM uses less interface bandwidth than overlay multicast.     

一种可运营的P2P网络模型的设计*

P2P网络是构建于物理网络拓扑之上的一层重叠网络.针对目前P2P网络模型中缺乏安全和管理机制的问题,提出一种新型P2P网络模型--基于拓扑感知的分层集中P2P网络模型.从可运营的角度出发,通过在此网络模型上引入用户认证管理和用户安全计费机制,实现了对用户网络行为的有效管理和监督.     

A Scalable Overlay Multicast Architecture for Large-Scale Applications

In this paper, we propose a two-tier overlay multicast architecture (TOMA) to provide scalable and efficient multicast support for various group communication applications. In TOMA, multicast service overlay network (MSON) is advocated as the backbone service domain, while end users in access domains form a number of small clusters, in which an application-layer multicast protocol is used for the communication between the clustered end users. TOMA is able to provide efficient resource utilization with less control overhead, especially for large-scale applications. It also alleviates the state scalability problem and simplifies multicast tree construction and maintenance when there are large numbers of groups in the network. To help MSON providers efficiently plan backbone service overlay, we suggest several provisioning algorithms to locate proxies, select overlay links, and allocate link bandwidth. Extensive simulation studies demonstrate the promising performance of TOMA     

结构化对等网络中面向信誉机制的资源共享平台

针对目前因特网上提高空闲网络资源利用率的需求,提出并描述了结构化对等网络中一种面向信誉机制的资源共享平台NRSP。NRSP系统可以把因特网上用户提交的各种作业高效地映射到平台中合适的网络资源上运行,充分共享空闲处理器的周期。该系统具有非集中性、可移植性、统计性与公平性的优点,使用分布式哈希表的Pastry网络来组织节点。同时提出一种新型的分布式信誉机制,支持资源的消费者与提供者之间交换信誉信息,完成信誉信息的统计。最后实现了NRSP的原型系统,通过对各种作业场景下的应用进行测试,性能分析表明NRSP采用公平的信誉机制可以更快、更多地共享其他节点的处理器周期。     

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.