Online social networks in economics

This paper describes how economists study social networks. While economists borrow from other fields like sociology or computer science, their approach of modeling of social networks is distinguished by the emphasis on the role of choices under constraints. Economists investigate how socioeconomic background and economic incentives affect the structure and composition of social networks. The characteristics of social networks are important for economic outcomes like the matching of workers to jobs and educational attainment. I review the theoretical and empirical literature that investigates these relationships and discuss possible implications of new, Internet based, forms of social interactions.     

Inferring user interests in microblogging social networks: a survey

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.     

A survey on privacy in decentralized online social networks

Decentralized Online Social Networks (DOSNs) have recently captured the interest of users because of the more control given to them over their shared contents. Indeed, most of the user privacy issues related to the centralized Online Social Network (OSN) services (such as Facebook or Google+) do not apply in the case of DOSNs because of the absence of the centralized service provider. However, these new architectures have motivated researchers to investigate new privacy solutions that allow DOSN’s users to protect their contents by taking into account the decentralized nature of the DOSNs platform.In this survey, we provide a comprehensive overview of the privacy solutions adopted by currently available DOSNs, and we compare them by exploiting several criteria. After presenting the differences that existing DOSNs present in terms of provided services and architecture, we identify, for each of them, the privacy model used to define the privacy policies and the mechanisms for their management (i.e., initialization and modification of the privacy policy). In addition, we evaluate the overhead introduced by the security mechanisms adopted for privacy policy management and enforcement by discussing their advantages and drawbacks.     

移动社交网络异常签到在线检测算法

随着智能手机、Pad等智能移动设备的广泛普及,移动社交网络的应用得到了快速发展。本文针对移动社交网络中用户异常签到位置检测问题,提出了一类基于用户移动行为特征的异常签到在线检测方法。首先,在基于距离的异常模型基础上,提出了基于历史位置（H-Outlier）和基于好友圈（F-Outlier）两种异常签到模型;然后,针对H-Outlier提出了一种优化的检测算法H-Opt,利用所提的签到状态模型与优化的邻居搜索机制降低检测时间;针对F-Outlier提出了一种基于触发的优化检测算法F-Opt,将连续的在线异常检测转化成了基于触发的异常检测方式;最后,在真实的移动社交网络用户签到数据集上,验证了所提算法的有效性。实验结果显示,F-Opt显著降低了H-Opt的异常检测错误率;同时,相比于LUE算法,F-Opt和H-Opt的效率分别平均提升了2.34倍和2.45倍。     

Online social networks for health behaviour change: Designing to increase socialization

Positive health behaviour by eating nutritious foods and performing physical activity has been shown to have significant benefit. Furthermore, theoretical models show that social factors contribute to health behaviour. However, social technology for health behaviour has provided limited social interaction. This paper presents an online social network for health behaviour change called VivoSpace that was designed from a theoretical foundation. The results from a field study (n = 35) are presented that include participants from both clinical and non-clinical settings. The results show that there was a significant change in some of the individual determinants for health behaviour change; however, social determinants did not change. Furthermore, the social features such as commenting were under utilized. Two follow-up focus groups (n = 7 and 8) were conducted to determine how the design should be iterated to increase socialization on VivoSpace. The results suggest that the posts need to add interest through system intelligence and allowing the user to add photos and other information to the post.     

Online control of discrete-event systems: A survey

In control theory, as in other areas of engineering research, there is an inherent tension between the breadth of a technique’s applicability and its mathematical tractability. For the area of discrete-event systems (DES), this manifested itself in a theory of supervisory control that originally provided correct-by-construction guarantees for offline solutions to a restricted kind of deterministic process. Follow-on work extended the reach of these techniques to a number of new settings, notably the development of online control without sacrificing any of the original DES performance guarantees. The ability to enact online control opened the door to applying DES techniques to the adaptive control processes presented by modern technologies: processes with dynamic and time-varying natures, whose characteristics may be understood poorly or not at all. Although many works have built on the seminal work of online control in DES, we believe that these ideas have not reached their full potential due to the difficulty in translating them to adjacent fields. In this survey, we look back at 30 years of research concerning the online control of DES and closely related limited lookahead policies with an eye to making the works accessible to practitioners in the broader control theory and artificial intelligence communities. We conclude with some thoughts on future research directions for the further development and application of online DES control techniques to problems requiring intelligent control in our modern world.     

Wireless sensor networks: a survey

This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.     

Neural networks in robotics: A survey

The purpose of this paper is to provide an overview of the research being done in neural network approaches to robotics, outline the strengths and weaknesses of current approaches, and predict future trends in this area.This work was supported, in part, by Sandia National Laboratories under contract No. 06-1977, Albuquerque, New Mexico.     

Anonymizing social networks: A generalization approach

Social networks provide a mathematical picture of various relationships that exist in society. A social network can be represented by graph data structures. These graphs are rich sources of information that must be published to share with the world. As a result, however, the privacy of users is compromised. Conventional techniques such as anonymization, randomization and masking are used to protect privacy. The techniques proposed to date do not consider the utility of published data. Absolute privacy implies zero utility, and vice versa. This paper focuses on the importance of users and the protection of their privacy. The importance of a user is determined by centrality or prestige measures. Generalization of the user is performed based on their importance to ensure privacy and utility in social networks. The information lost due to generalization is also measured.     

RFID enabled traceability networks: a survey

The emergence of radio frequency identification (RFID) technology brings significant social and economic benefits. As a non line of sight technology, RFID provides an effective way to record movements of objects within a networked RFID system formed by a set of distributed and collaborating parties. A trail of such recorded movements is the foundation for enabling traceability applications. While traceability is a critical aspect of majority of RFID applications, realizing traceability for these applications brings many fundamental research and development issues. In this paper, we assess the requirements for developing traceability applications that use networked RFID technology at their core. We propose a set of criteria for analyzing and comparing the current existing techniques including system architectures and data models. We also outline some research opportunities in the design and development of traceability applications.     

NodeTrix: a hybrid visualization of social networks

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.     

Vulnerabilities in cognitive radio networks: A survey

Cognitive radio networks are envisioned to drive the next generation wireless networks that can dynamically optimize spectrum use. However, the deployment of such networks is hindered by the vulnerabilities that these networks are exposed to. Securing communications while exploiting the flexibilities offered by cognitive radios still remains a daunting challenge. In this survey, we put forward the security concerns and the vulnerabilities that threaten to plague the deployment of cognitive radio networks. We classify various types of vulnerabilities and provide an overview of the research challenges. We also discuss the various techniques that have been devised and analyze the research developments accomplished in this area. Finally, we discuss the open research challenges that must be addressed if cognitive radio networks were to become a commercially viable technology.     

Position-based routing in vehicular networks: A survey

Routing in vehicular network is a challenging task due to network partitioning, high vehicular speed, and city environment characteristics. These characteristics results in degraded performance in traditional routing protocols. Traditional routing protocols, addressing the issues of mobile ad hoc network, are applicable for MANET applications. Position-based routing protocols, which are mostly based on greedy routing, are more suited to highly dynamic and mobile network. In this paper, we survey state of art routing protocols previously used in vehicular networks, present open research challenges and possible future direction. We categorize protocols into two categories based on their communicating mode (vehicle-to-vehicle, vehicle-to-infrastructure) irrespective of their simulating environment (highway, urban). Both vehicle-to-vehicle and vehicle-to-infrastructure communication provides connectivity based on multi-hop paradigm in a seamless way. We discuss pros and cons for routing protocols belonging to each category. By doing qualitative comparison of routing protocols, it is observed that hybrid communication would be the better choice for both communication mode operable in either a city environment or an open environment.     

Wireless sensor networks for healthcare: A survey

Becoming mature enough to be used for improving the quality of life, wireless sensor network technologies are considered as one of the key research areas in computer science and healthcare application industries. The pervasive healthcare systems provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. This minimizes the need for caregivers and helps the chronically ill and elderly to survive an independent life, besides provides quality care for the babies and little children whose both parents have to work. Although having significant benefits, the area has still major challenges which are investigated in this paper. We provide several state of the art examples together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provide a comprehensive analysis of the benefits and challenges of these systems.     

Online and off-line handwriting recognition: a comprehensive survey

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered     

Learning in linear neural networks: a survey

Networks of linear units are the simplest kind of networks, where the basic questions related to learning, generalization, and self-organization can sometimes be answered analytically. We survey most of the known results on linear networks, including: 1) backpropagation learning and the structure of the error function landscape, 2) the temporal evolution of generalization, and 3) unsupervised learning algorithms and their properties. The connections to classical statistical ideas, such as principal component analysis (PCA), are emphasized as well as several simple but challenging open questions. A few new results are also spread across the paper, including an analysis of the effect of noise on backpropagation networks and a unified view of all unsupervised algorithms.     

Event analysis in social multimedia: a survey

Recent years have witnessed the rapid growth of social multimedia data available over the Internet. The age of huge amount of media collection provides users facilities to share and access data, while it also demands the revolution of data management techniques, since the exponential growth of social multimedia requires more scalable, effective and robust technologies to manage and index them. The event is one of the most important cues to recall people’s past memory. The reminder value of an event makes it extremely helpful in organizing data. The study of event based analysis on social multimedia data has drawn intensive attention in research community. In this article, we provide a comprehensive survey on event based analysis over social multimedia data, including event enrichment, detection, and categorization. We introduce each paradigm and summarize related research efforts. In addition, we also suggest the emerging trends in this research area.