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.     

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.     

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.     

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.     

Point-to-point voice over ad hoc networks: A survey

The dynamic topologies of mobile and wireless ad hoc networks affect voice communication applications. Difficult issues are wireless links with time-varying capacity and large loss rates, available bandwidth undergoing fast time-scale variations due to channel fading and physical obstacles, absence of centralized components assisting session set up and management, and instability of routes. The purpose of this paper is to survey research works that have been conducted to address these difficulties with an emphasis on the support of point-to-point voice sessions. The survey covers six complementary aspects: voice over ad hoc network architecture, route selection, bandwidth reservation and admission control, adaptive applications and security.     

A clique-superposition model for social networks

How does a social network evolve? Sociologists have studied this question for many years.According to some famous sociologists,social links are driven by social intersections.Actors who affiliate with the shared intersections tend to become interpersonally linked and form a cluster.In the social network,an actor cluster could be a clique or a group of several smaller-sized cliques.Thus we can conclude that a social network is composed of superposed cliques of different sizes.However,sociologists did not verify the theory in large scale data due to lack of computing ability.Motivated by this challenge,incorporated with the theory,we utilize data mining technologies to study the evolution patterns of large scale social networks in real world.Then,we propose a novel Clique-superposition generative model,which generates undirected weighted networks.By extensive experiments,we demonstrate that our model can generate networks with static and time evolving patterns observed not only in earlier literature but also in our work.     

Synchronization in complex networks of phase oscillators: A survey

The emergence of synchronization in a network of coupled oscillators is a fascinating subject of multidisciplinary research. This survey reviews the vast literature on the theory and the applications of complex oscillator networks. We focus on phase oscillator models that are widespread in real-world synchronization phenomena, that generalize the celebrated Kuramoto model, and that feature a rich phenomenology. We review the history and the countless applications of this model throughout science and engineering. We justify the importance of the widespread coupled oscillator model as a locally canonical model and describe some selected applications relevant to control scientists, including vehicle coordination, electric power networks, and clock synchronization. We introduce the reader to several synchronization notions and performance estimates. We propose analysis approaches to phase and frequency synchronization, phase balancing, pattern formation, and partial synchronization. We present the sharpest known results about synchronization in networks of homogeneous and heterogeneous oscillators, with complete or sparse interconnection topologies, and in finite-dimensional and infinite-dimensional settings. We conclude by summarizing the limitations of existing analysis methods and by highlighting some directions for future research.     

MatrixExplorer: a dual-representation system to explore social networks

MatrixExplorer is a network visualization system that uses two representations: node-link diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices are commonly used in social networks analysis, very few systems support the matrix-based representations to visualize and analyze networks. MatrixExplorer provides several novel features to support the exploration of social networks with a matrix-based representation, in addition to the standard interactive filtering and clustering functions. It provides tools to reorder (layout) matrices, to annotate and compare findings across different layouts and find consensus among several clusterings. MatrixExplorer also supports node-link diagram views which are familiar to most users and remain a convenient way to publish or communicate exploration results. Matrix and node-link representations are kept synchronized at all stages of the exploration process     

在线社交网络研究概述

在线社交网络是伴随着互联网技术发展产生的,它属于众多复杂网络中的一种。近年来,对于在线社交网络的研究不断深入,研究方向可以细分为网络拓扑特征的分析、虚拟社区划分算法的研究、传播动力学研究、网络采样与重构、网络拓扑识别等。大数据研究的兴起使得在线社交网络的研究更加受到人们的关注。当前,人们的日常生活几乎离不开在线社交网络,也因此每天都会有大量的用户数据产生,分析、利用这些数据可以帮助人们了解自己并创造更多的价值。