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1.
针对学术社交网络独有的社交性,构建了基于社区划分的学术论文推荐模型。模型选择社区复杂好友关系网络图中最大连通分量作为数据处理逻辑单元,在此基础上进行核心关系网划分,并采用非参数控制的方式,在所建立的核心关系网内建立标签,在学术社交网络中通过标签传播进行社区划分,根据社区划分结果在社区内部的用户之间推荐学术论文。该社区划分算法与经典社区划分算法在人工网络上进行仿真实验,结果表明该算法在不同特征的人工网络上皆能取得良好的社区发现质量。  相似文献   

2.
计算机网络安全形势严峻,对实施网络攻击的黑客以及黑客所在组织的研究越来越重要。社交网络有不受时间空间限制的特点,因此成为黑客交流的主要平台,也是网络安全研究人员获取信息的重要渠道。为了对社交网络中的黑客进行分析,文章提出一种基于社区发现的社交网络关键黑客节点识别方法。首先,文章通过图卷积网络以无监督方式实现网络的社区划分;然后,利用用户之间的交互行为和主题相似度,通过改进的Page Rank算法实现社区内黑客节点的影响力度量;最后,通过独立级联模型评估关键黑客节点对网络传播效率的作用。在Twitter数据集上的实验表明,该方法能有效识别社交网络中的关键黑客用户。  相似文献   

3.
正小众社交不比拥有海量用户的Twitter、微博,它们凭借自己独特的魅力吸引用户并维持着独特的关系。无论是Facebook、Twitter抑或微博,在这些大型社交网络里,人们时常会迷失在海量信息中。通常会有类似情况,一个用户在微博、人人、朋友圈发布一样的信息,看的可能也是同样的人。随着圈子越来越复杂,用户不得不开始顾及别人的想法,不能像当初那样畅所欲言。诚然,面向社会的大众社交网站正在遭受目标受众狭窄但清晰的小众网站的冲击和分流。小众社交不比拥有海量用户的Twitter、微博,它们凭借自己独特的魅力吸引用户并维持着独特的关系。ArtStack(艺推)是艺术家专属的社交网络,结合了Pinterest,Twitter和Facebook的  相似文献   

4.
社交网络作为一种交往方式,已经深入人心。其用户数据在这个大数据时代蕴藏着大量的价值。随着Twitter API的开放,社交网络Twitter俨然成为一个深受欢迎的研究对象,而用户影响力更是其中的研究热点。PageRank算法计算用户影响力已经由来已久,但是它太依赖于用户之间的关注关系,排名不具备时效性。引入用户活跃度的改进PageRank算法,具备一定的时效性,但是不具有足够的说服力和准确性。研究了一种新的基于时间分布用户活跃度的ABP算法,并为不同时段的活跃度加以相应的时效权重因子。最后,以Twitter为研究对象,结合社交关系网,通过实例分析说明ABP算法更具时效性和说服力,可以比较准确地提高活跃用户的排名,降低非活跃用户排名。  相似文献   

5.
一种基于因子图模型的半监督社区发现方法   总被引:3,自引:0,他引:3  
社区发现是社交网络分析中一个重要的研究方向.当前大部分的研究都聚焦在自动社区发现问题,但是在具有数据缺失或噪声的网络中,自动社区发现算法的性能会随着噪声数据的增加而迅速下降.通过在社区发现中融合先验信息,进行半监督的社区发现,有望为解决上述挑战提供一条可行的途径.本文基于因子图模型,通过融入先验信息到一个统一的概率框架中,提出了一种基于因子图模型的半监督社区发现方法,研究具有用户引导情况下的社交网络社区发现问题.在三个真实的社交网络数据(Zachary社会关系网、海豚社会网和DBLP协作网)上进行实验,证明通过融入先验信息可以有效地提高社区发现的精度,且将我们的方法与一种最新的半监督社区发现方法(半监督Spin-Glass模型)进行对比,在三个数据集中F-measure平均提升了6.34%、16.36%和12.13%.  相似文献   

6.
社交网络中感知技术的研究与应用   总被引:1,自引:0,他引:1  
社交网络已成为互联网上最热门的话题和网络应用亮点,它让用户组织自己的网络链接,维护各种社会关系.社交网络重要的是对个人信息的维护,对网络内他人信息的感知;在社交网络环境下,用户的信息感知程度普遍较低.探索了是否可有效调整CSCW领域中的感知概念以应用到社会网络领域.分析了感知的概念和内涵,对比了CSCW领域的群组与社交网络中的社区,研究了社交网络感知信息的形成过程,从社交网络环境和群组两个方面讨论了感知技术的应用,改善了社交网络中的通信,增强了用户之间的交互性.最后,实现了面向科研工作者的社交网络--学术社区,在学术社区中应用感知技术,帮助研究者发现科研热点或某一领域的研究群体,促进学术交流和创新.  相似文献   

7.
微博是当前最流行的在线社交媒体之一,有效地检测出微博用户的社区结构,能够帮助人们理解微博社交网络的结构和用户的行为特征,从而为用户提供个性化的服务。然而,现有社区检测算法大多只考虑社交网络节点之间的直接链接关系,忽略节点自身的内容特征。针对此问题,提出一种基于增广网络的快速微博社区检测算法。该算法通过融合社交网络的链接信息以及用户在微博上所发布的博文内容信息构建增广网络,然后以模块度为目标函数快速挖掘增广网络中的主题社区。通过真实微博社交网络的实验表明,提出的算法能够高效地检测出社交网络的主题社区。
  相似文献   

8.
为解决传统社区发现算法难适用于大型复杂异质的移动网络的问题,利用移动网络使用详单数据(Usage Detail Record, UDR)和移动用户社交数据构建网络模型,提出一种融合多维信息的移动社区发现方法BNMF-NF。该方法综合考虑用户社交关系和时空行为,给出用户社交相似度、位置分布相似度和主题偏好相似度,利用加权网络融合方法融合多维相似关系构建用户相似网络,并运用有界非负矩阵分解技术实现社区结构的检测。在Foursquare和电信数据集上的实验结果表明,BNMF-NF方法能够有效发现移动网络中用户社区结构。  相似文献   

9.
随着以用户为中心的Web 2.0的发展,社交网络平台以惊人的影响力渗入到生活的方方面面,对社交网络中的内容进行情感分析已经成为热点研究课题。Twitter、新浪微博等在线社交网站吸引了大量用户,通过用户间的交互,产生了许多包含用户间社会关系的信息,并且这些社会关系被广泛应用于社交网络的情感分析。融合社会关系的社交网络情感分析将用户间交互形成的社会关系应用到对用户发表在社交网络上内容的情感分析中,拟解决文本短小精炼、语义模糊、特征较为稀疏带来的情感分析准确率低的问题。对融合社会关系的社交网络情感分析研究进展进行综述,梳理、分析主要的方法,列举出其中的关键问题,最后阐述了研究趋势和展望,并进行了总结。  相似文献   

10.
在线知识社区中,问题的回答可以看作多个回答者用户(领域专家)之间的协作行为。协作行为在知识社区中通常是大规模地发生,协作行为预测对在线社交中领域专家的推荐有重要意义。基于在线知识社区中回答者用户之间的协作行为,构建以领域专家为节点,以他们之间的协作回答关系为边的协作网络。由于协作行为网络的构建与社交关系网络的构建上结构的相似性,可以将协作行为预测构建为协作网络中的链接预测问题。通过构建基于图卷积神经网络的链接预测模型,对在线知识社区中回答者用户的协作行为进行预测。基于“知乎”数据集的实验验证,与其他经典的预测方法进行比较时,发现提出的方法能够更加有效地预测在线知识社区中回答者用户之间的协作行为。  相似文献   

11.
目前,Twitter的广告投放市场巨大,但针对个性化的广告投放却很少,提出一种基于星形社区模型的广告投放方式.采用网页爬虫获取Twitter用户社交信息,利用高斯模型的多因素权系数算法处理用户社交信息,初步筛选出对产品感兴趣和有影响力的用户,并对其建立星形结构模型,二次筛选,确定出度核心节点并识别出目标星形子图社区,将该社区的出度核心节点作为广告投放载体进行个性化的投放.实验结果表明该广告投放方式具有较高的社区用户满意度.  相似文献   

12.
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.  相似文献   

13.
Decreasing revenues and increasing expenses has led many healthcare organizations to adopt newer technological applications in order to address the informational needs of their patients. One such adoption technique is to develop a more robust e-patient environment. Health care organizations may increase their effectiveness in meeting the needs of a growing e-patient population through the implementation of high-quality social networking applications such as Twitter. These applications may help to support and maintain a valuable and informed community. A literature review identifies three characteristics that have an impact on information exchange inherent to social networks: number of members, contact frequency, and type of knowledge. Data from a case study of a juvenile diabetic using Twitter helps to demonstrate these aforementioned characteristics. A framework is developed that may be used by health care organizations to better align social network objectives with expectations of an End user community (EUCY). Managerial implications of this study are discussed that can help information technology professionals as well as health administrators when implementing social networks.  相似文献   

14.
Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.  相似文献   

15.
Liu  Bo  Ni  Zeyang  Luo  Junzhou  Cao  Jiuxin  Ni  Xudong  Liu  Benyuan  Fu  Xinwen 《World Wide Web》2019,22(6):2953-2975

Social networking websites with microblogging functionality, such as Twitter or Sina Weibo, have emerged as popular platforms for discovering real-time information on the Web. Like most Internet services, these websites have become the targets of spam campaigns, which contaminate Web contents and damage user experiences. Spam campaigns have become a great threat to social network services. In this paper, we investigate crowd-retweeting spam in Sina Weibo, the counterpart of Twitter in China. We carefully analyze the characteristics of crowd-retweeting spammers in terms of their profile features, social relationships and retweeting behaviors. We find that although these spammers are likely to connect more closely than legitimate users, the underlying social connections of crowd-retweeting campaigns are different from those of other existing spam campaigns because of the unique features of retweets that are spread in a cascade. Based on these findings, we propose retweeting-aware link-based ranking algorithms to infer more suspicious accounts by using identified spammers as seeds. Our evaluation results show that our algorithms are more effective than other link-based strategies.

  相似文献   

16.
Microblogging(e.g. Twitter, http://twitter.com), as a new form of online communication in which users talk about their daily lives, publish opinions or share information by short posts, has become one of the most popular social networking services today, which makes it potentially a large information base attracting increasing attention of researchers in the field of knowledge discovery and data mining. In this paper, we conduct a survey about existing research on information extraction from microblogging services and their applications, and then address some promising future works. We specifically analyze three types of information: personal, social and travel information.  相似文献   

17.
Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.  相似文献   

18.
Over the past few years, a large and ever increasing number of Web sites have incorporated one or more social login platforms and have encouraged users to log in with their Facebook, Twitter, Google, or other social networking identities. Research results suggest that more than two million Web sites have already adopted Facebook’s social login platform, and the number is increasing sharply. Although one might theoretically refrain from such social login features and cross-site interactions, usage statistics show that more than 250 million people might not fully realize the privacy implications of opting-in. To make matters worse, certain Web sites do not offer even the minimum of their functionality unless users meet their demands for information and social interaction. At the same time, in a large number of cases, it is unclear why these sites require all that personal information for their purposes. In this paper, we mitigate this problem by designing and developing a framework for minimum information disclosure in social login interactions with third-party sites. Our example case is Facebook, which combines a very popular single sign-on platform with information-rich social networking profiles. Whenever users want to browse to a Web site that requires authentication or social interaction using a Facebook identity, our system employs, by default, a Facebook session that reveals the minimum amount of information necessary. Users have the option to explicitly elevate that Facebook session in a manner that reveals more or all of the information tied to their social identity. This enables users to disclose the minimum possible amount of personal information during their browsing experience on third-party Web sites.  相似文献   

19.
This paper proposes the concept and the technology of social networking federation as a paradigm where information on various social network systems can be seamlessly integrated in order to provide users a uniform and semantic view of their social connections. Such a uniformly fused social network provides a single point of access where all information with respect to one’s social networks can be queried and reasoned about. The goal of the research is to establish a foundation of integrating and assimilating information within multiple social network systems. We designed a reference model of social networking federation system in this paper, as well as some prototype application to demonstrate its paradigm. We believe our work would provide a novel vision of future online social network.  相似文献   

20.
With the rise of social networking services such as Facebook and Twitter, the problem of spam and content pollution has become more significant and intractable. Using social networking services, users are able to develop relationships and share messages with others in a very convenient manner; however, they are vulnerable to receiving spam messages. The automatic detection of spammers or content polluters on the network can effectively reduce the burden on the service provider in making a decision on appropriate counteractions. Content polluters can be automatically identified by using the supervised learning technique of artificial intelligence. To build a classification model with high accuracy automatically from the training data set, it is important to identify a set of useful features that can classify polluters and non-polluters. Moreover, because we deal with a huge amount of raw data in this process, the efficiency of data preparation and model creation are also critical issues that need to be addressed. In this paper, we present an efficient method for detecting content polluters on Twitter. Specifically, we propose a set of features that can be easily extracted from the messages and behaviors of Twitter users and construct a new breed of classifiers based on these features. The proposed approach requires only a minimal number of feature values per Twitter user and thus adds considerably less time to the overall mining process compared to other methods. Experiments confirm that the proposed approach outperforms previous approaches in both classification accuracy and processing time.  相似文献   

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