A traffic analysis attack to compute social network measures

The development of digital media, the increasing use of social networks, the easier access to modern technological devices, is perturbing thousands of people in their public and private lives. People love posting their personal news without consider the risks involved. Privacy has never been more important. Privacy enhancing technologies research have attracted considerable international attention after the recent news against users personal data protection in social media websites like Facebook. It has been demonstrated that even when using an anonymous communication system, it is possible to reveal user’s identities through intersection attacks or traffic analysis attacks. Combining a traffic analysis attack with Analysis Social Networks (SNA) techniques, an adversary can be able to obtain important data from the whole network, topological network structure, subset of social data, revealing communities and its interactions. The aim of this work is to demonstrate how intersection attacks can disclose structural properties and significant details from an anonymous social network composed of a university community.

     

Reconstructing social interactions using an unreliable wireless sensor network

In the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed for their analysis. These works have led to the identification of similarities in the structures of such networks arising in very different fields, and to the development of a body of knowledge, tools and methods for their study.While many interesting questions remain open on the subject of static networks, challenging issues arise from the study of dynamic networks. In particular, the measurement, analysis and modeling of social interactions are first class concerns.In this article, we address the challenges of capturing physical proximity and social interaction by means of a wireless network. In particular, as a concrete case study, we exhibit the deployment of a wireless sensor network applied to the measurement of health care workers’ exposure to tuberculosis-infected patients in a service unit of the Bichat-Claude Bernard hospital in Paris, France. This network has continuously monitored the presence of all HCWs in all rooms of the service during a three month period.We both describe the measurement system that was deployed and some early analysis on the measured data. We highlight the bias introduced by the measurement system reliability and provide a reconstruction method which not only leads to a significantly more coherent and realistic dataset but also evidences phenomena a priori hidden in the raw data. By this analysis, we suggest that a processing step is required prior to any adequate exploitation of data gathered thanks to a non-fully reliable measurement architecture.     

Semantic web-based social network access control

The existence of online social networks that include person specific information creates interesting opportunities for various applications ranging from marketing to community organization. On the other hand, security and privacy concerns need to be addressed for creating such applications. Improving social network access control systems appears as the first step toward addressing the existing security and privacy concerns related to online social networks. To address some of the current limitations, we have created an experimental social network using synthetic data which we then use to test the efficacy of the semantic reasoning based approaches we have previously suggested.     

Context-aware trust network extraction in large-scale trust-oriented social networks

In recent years, social networking sites have been used as a means for a rich variety of activities, such as movie recommendations and product recommendations. In order to evaluate the trust between a truster (i.e., the source) and a trustee (i.e., the target) who have no direct interaction in Online Social Networks (OSNs), the trust network between them that contains important intermediate participants, the trust relations between the participants, and the social context, has an important influence on trust evaluation. Thus, to deliver a reasonable trust evaluation result, before performing any trust evaluation (i.e., trust transitivity), the contextual trust network from a given source to a given target needs to be first extracted from the social network, where constraints on social context should also be considered to guarantee the quality of the extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first present a contextual trust-oriented social network structure which takes social contextual impact factors, including trust, social intimacy degree, community impact factor, preference similarity and residential location distance into account. These factors have significant influences on both social interactions between participants and trust evaluation. Then, we present a new concept QoTN (Quality of Trust Network) and propose a social context-aware trust network extraction model. Finally, we propose a Heuristic Social Context-Aware trust Network extraction algorithm (H-SCAN-K) by extending the K-Best-First Search (KBFS) method with several proposed optimization strategies. The experiments conducted on two real datasets illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust networks.     

Community detection for emerging social networks

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.     

Parallel quantum-inspired evolutionary algorithms for community detection in social networks

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers.In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization.In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs.The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup.     

学术社交网络中的权威学者推荐模型

目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。     

A robust ant colony optimization-based algorithm for community mining in large scale oriented social graphs

Community detection plays a key role in such important fields as biology, sociology and computer science. For example, detecting the communities in protein–protein interactions networks helps in understanding their functionalities. Most existing approaches were devoted to community mining in undirected social networks (either weighted or not). In fact, despite their ubiquity, few proposals were interested in community detection in oriented social networks. For example, in a friendship network, the influence between individuals could be asymmetric; in a networked environment, the flow of information could be unidirectional. In this paper, we propose an algorithm, called ACODIG, for community detection in oriented social networks. ACODIG uses an objective function based on measures of density and purity and incorporates the information about edge orientations in the social graph. ACODIG uses ant colony for its optimization. Simulation results on real-world as well as power law artificial benchmark networks reveal a good robustness of ACODIG and an efficiency in computing the real structure of the network.     

学术社交网络中的权威学者推荐模型

目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。     

Overlapping community detection in social networks using coalitional games

Community detection is a significant research problem in various fields such as computer science, sociology and biology. The singular characteristic of communities in social networks is the multimembership of a node resulting in overlapping communities. But dealing with the problem of overlapping community detection is computationally expensive. The evolution of communities in social networks happens due to the self-interest of the nodes. The nodes of the social network acts as self-interested players, who wish to maximize their benefit through interactions in due course of community formation. Game theory provides a systematic framework tox capture the interactions between these selfish players in the form of games. In this paper, we propose a Community Detection Game (CDG) that works under the cooperative game framework. We develop a greedy community detection algorithm that employs Shapley value mechanism and majority voting mechanism in order to disclose the underlying community structure of the given network. Extensive experimental evaluation on synthetic and real-world network datasets demonstrates the effectiveness of CDG algorithm over the state-of-the-art algorithms.     

Development of computer-supported collaborative social networks in a distributed learning community

This study examines the formation and change of collaborative learning social networks in a distributed learning community. A social network perspective is employed to understand how collaborative networks evolved over time when 31 distributed learners collaborated on a design project using a computer-mediated communication system during two semesters. Special attention was paid to how pre-existing friendship networks influenced the formation of macro-level collaborative learning networks and individual level social capital. We discovered that pre-existing friendship networks significantly influenced the formation of collaborative learning networks, but the effect was dependent on the developmental phase of community. Also, pre-existing networks generally acted as a social liability that constrained learners' ability to enhance their social networks and build social capital when they participated in a new learning environment. The results suggest that, in order to fully understand how to build effective collaborative learning and work environments, participants' social network structures need to be considered.     

Creation and growth of online social network

Social networks are an example of complex systems consisting of nodes that can interact with each other and based on these activities the social relations are defined. The dynamics and evolution of social networks are very interesting but at the same time very challenging areas of research. In this paper the formation and growth of one of such structures extracted from data about human activities within online social networking system is investigated. Dynamics of both local and global characteristics are studied. Analysis of the dynamics of the network growth showed that it changes over time—from random process to power-law growth. The phase transition between those two is clearly visible. In general, node degree distribution can be described as the scale-free but it does not emerge straight from the beginning. Social networks are known to feature high clustering coefficient and friend-of-a-friend phenomenon. This research has revealed that in online social network, although the clustering coefficient grows over time, it is lower than expected. Also the friend-of-a-friend phenomenon is missing. On the other hand, the length of the shortest paths is small starting from the beginning of the network existence so the small-world phenomenon is present. The unique element of the presented study is that the data, from which the online social network was extracted, represents interactions between users from the beginning of the social networking site existence. The system, from which the data was obtained, enables users to interact using different communication channels and it gives additional opportunity to investigate multi-relational character of human relations.     

D-HOCS: an algorithm for discovering the hierarchical overlapping community structure of a social network

Social networks often demonstrate a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities, i.e. communities in social networks may be overlapping. In this paper, we define a hierarchical overlapping community structure to present overlapping communities of a social network at different levels of granularity. Discovering the hierarchical overlapping community structure of a social network can provide us a deeper understanding of the complex nature of social networks. We propose an algorithm, called D-HOCS, to derive the hierarchical overlapping community structure of social networks. Firstly, D-HOCS generates a probability transition matrix by applying random walk to a social network, and then trains a Gaussian Mixture Model using the matrix. Further D-HOCS derives overlapping communities by analyzing mean vectors of the Gaussian mixture model. Varying the number of components, D-HOCS repeatedly trains the Gaussian mixture model, detecting the overlapping communities at different levels of granularity. Organizing the overlapping communities into a hierarchy, D-HOCS can finally obtain the hierarchical overlapping community structure of the social network. The experiments conducted on synthetic and real dataset demonstrate the feasibility and applicability of the proposed algorithm. We further employ D-HOCS to explore Enron e-mail corpus, and obtain several interesting insights. For example, we find out a coordinator who coordinated many sections of the Enron Corporation to complete an important task during first half of 2001. We also identify a community that corresponds to a real organization in Enron Corporation.     

How to keep members using the information in a computer-supported social network

Computer-supported social networks have become a significant channel for people to interact and exchange information. The success of computer-supported social networks depends on the extent to which members will stay and continue participating. Many computer-supported social networks however suffer from the problem of retaining members. Drawing from theories of user satisfaction and information adoption, we develop a model to examine how computer-supported social networks encourage members to continue participating and using the information in the network. The theoretical model is validated through an online survey of 240 users of a Bulletin Board System established by a local university in China. The results reveal that individuals will continue to use the information in a computer-supported social network when they are satisfied with their prior usage, and when they perceive that the information in the network is useful. The results also suggest that individuals’ perceived information usefulness and satisfaction are determined by information quality and source credibility in the context of computer-supported social networks. Theoretical and practical implications about computer-supported social networks are discussed.     

An e-patient’s End-user community (EUCY): The value added of social network applications

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.     

微博社交网络模型的建立及其性质研究

针对在线社交网络进行建模研究将有助于理解其网络特征结构和演化机制,为了提高网络模型描述在线社交网络的准确性,分析统计了新浪微博网络演化相关特征,并结合复杂网络中社团结构特征和优先连接特性提出了COMW（Community-Oriented Model for Weibo）网络演化模型。通过实验模拟验证了COMW模型的包括度分布、聚类系数、网络效率、社团结构演化等网络特征。实验表明,COMW模型具有明显的小世界特性和明显的社团结构,并在多项特征上均符合微博网络,能够较为合理地表征微博网络的演化。     

Finding overlapping communities in a complex network of social linkages and Internet of things

Online social networks play an important role in today’s Internet. These social networks contain huge amounts of data and the integrated framework of SN with Internet of things (IoT) presents a challenging problem. IoT is the ubiquitous interconnection of everyday items of interest (things), providing connectivity anytime, anywhere, and with anything. Like biological, co-authorship, and virus-spread networks, IoT and Social Network (SN) can be characterized to be complex networks containing substantial useful information. In the past few years, community detection in graphs has been an active area of research (Lee and Won in Proceedings of IEEE SoutheastCon, pp. 1–5, 2012). Many graph mining algorithms have been proposed, but none of them can help in capturing an important dimension of SNs, which is friendship. A friend circle expands with the help of mutual friends, and, thus, mutual friends play an important role in social networks’ growth. We propose two graph clustering algorithms: one for undirected graphs such as Facebook and Google+, and the other for directed graphs such as Twitter. The algorithms extract communities, and based on the access control policy nodes share resources (things). In the proposed Community Detection in Integrated IoT and SN (CDIISN) algorithm, we divide the nodes/actors of complex networks into basic, and IoT nodes. We, then, execute the community detection algorithm on them. We take nodes of a graph as members of a SN, and edges depicting the relations between the nodes. The CDIISN algorithm is purely deterministic, and no fuzzy communities are formed. It is known that one community detection algorithm is not suitable for all types of networks. For different network structures, different algorithms exhibit different results, and methods of execution. However, in our proposed method, the community detection algorithm can be modified as desired by a user based on the network connections. The proposed community detection approach is unique in the sense that a user can define his community detection criteria based on the kind of network.     

The use of an artificial neural network to detect automatically managed accounts in social networks

The problem of detection of automatically managed accounts (bots) in social networks has been considered. The method of their detection based on machine learning methods is proposed. The paper describes an example of a method based on artificial neural network learning. The parameters of a page in a social network used to detect bots have been presented. An experimental evaluation of the proposed system performance is given that demonstrates a high level of detection of bots in social networks.     

社交网络中的社团隐私研究综述

社团是社交网络的重要特征,社团检测技术的发展给网络用户带来隐私泄露的危险.如何保护敏感的社团信息不被泄露,保障用户与社团安全已经成为网络安全领域的研究热点.近几年,社团保护技术取得了初步进展,但针对社交网络中的社团隐私或社团安全研究进展综述较少,不利于该研究方向的长远发展.因此,主要针对社团结构隐私方面的研究进展进行综...     

Intelligent social network analysis using granular computing

An introduction to some basic ideas of graph (relational network) theory is first provided. We then discuss some concepts from granular computing in particular the fuzzy set paradigm of computing with words. The natural connection between graph theory and granular computing, particularly fuzzy set theory, is pointed out. This connection is grounded in the fact that these are both set‐based technologies. Our objective here is to take a step toward the development of intelligent social network analysis using granular computing. In particular one can start by expressing in a human‐focused manner concepts associated with social networks then formalize these concepts using fuzzy sets and then evaluate these concepts with respect to social networks that have been represented using set‐based relational network theory. We capture this approach in what we call the paradigm for intelligent social network analysis, PISNA. Using this paradigm, we provide definitions of a number of concepts related to social networks. © 2008 Wiley Periodicals, Inc.