Consensus seeking in a network of discrete-time linear agents with communication noises

This paper studies the mean square consensus of discrete-time linear time-invariant multi-agent systems with communication noises. A distributed consensus protocol, which is composed of the agent's own state feedback and the relative states between the agent and its neighbours, is proposed. A time-varying consensus gain a[k] is applied to attenuate the effect of noises which inherits in the inaccurate measurement of relative states with neighbours. A polynomial, namely ‘parameter polynomial’, is constructed. And its coefficients are the parameters in the feedback gain vector of the proposed protocol. It turns out that the parameter polynomial plays an important role in guaranteeing the consensus of linear multi-agent systems. By the proposed protocol, necessary and sufficient conditions for mean square consensus are presented under different topology conditions: (1) if the communication topology graph has a spanning tree and every node in the graph has at least one parent node, then the mean square consensus can be achieved if and only if ∑^∞_{k = 0}a[k] = ∞, ∑^∞_{k = 0}a²[k] < ∞ and all roots of the parameter polynomial are in the unit circle; (2) if the communication topology graph has a spanning tree and there exits one node without any parent node (the leader–follower case), then the mean square consensus can be achieved if and only if ∑^∞_{k = 0}a[k] = ∞, lim_{k → ∞}a[k] = 0 and all roots of the parameter polynomial are in the unit circle; (3) if the communication topology graph does not have a spanning tree, then the mean square consensus can never be achieved. Finally, one simulation example on the multiple aircrafts system is provided to validate the theoretical analysis.     

Customer flows in a network with two types of service

Methods of network analysis that consider the differences in the request service time are proposed. Busy periods in queuing systems with heterogeneities are studied. The network has two types of requests differing in their service time, which follows an exponential distribution. An example is given of a network interpreting the interaction of local area network servers.     

Privacy threats in a mobile enterprise social network

The ‘Instant Knowledge’ system is an enterprise based social network that aims to introduce employees of the enterprise to contacts within the organization who may have skills relevant to particular tasks. The skills database is maintained through context-aware devices, and mobile devices in particular. The aim is to populate the database automatically based on user context data and to provide automatic introductions, again based on context data. This paper examines the security and privacy implications of this system and shows that while threat modelling on its own provides a solid base from which to secure the system, this is not enough to ensure that all privacy issues are considered. This is demonstrated by applying a mis-use case analysis that shows how personal identifying information can be inadvertantly leaked to malicious parties.     

Segregation discovery in a social network of companies

We introduce a framework for the data-driven analysis of social segregation of minority groups, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem is introduced, which consists of searching sub-groups of population and minorities for which a segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem by computing a multi-dimensional data cube that can be explored by the analyst. The machinery underlying the search algorithm relies on frequent itemset mining concepts and tools. The framework is challenged on a cases study in the context of company networks. We analyse segregation on the grounds of sex and age for directors in the boards of the Italian companies. The network includes 2.15M companies and 3.63M directors.     

Influence maximization with limit cost in social network

Social networking service (SNS) applications are changing the way information spreads in online communities. As real social relationships are projected into SNS applications, word of mouth has been an important factor in the information spreading processes of those applications. By assuming each user needs a cost to accept some specific information, this paper studies the initial "seed user" selection strategy to maximize information spreading in a social network with a cost budget. The main contributions of this paper are: 1) proposing a graphic SEIR model (gSEIR) by extending the epidemic compartmental model to simulate the dynamic information spreading process between individuals in the social network; 2) proposing a formal definition for the influence maximization problem with limit cost (IMLC) in social networks, and proving that this problem can be transformed to the weighted set-cover problem (WSCP) and thus is NP-Complete; 3) providing four different greedy algorithms to solve the IMLC problem; 4) proposing a heuristic algorithm based on the method of Lagrange multipliers (HILR) for the same problem; 5) providing two parts of experiments to test the proposed models and algorithms in this paper. In the first part, we verify that gSEIR can generate similar macro-behavior as an SIR model for the information spreading process in an online community by combining the micro-behaviors of all the users in that community, and that gSEIR can also simulate the dynamic change process of the statuses of all the individuals in the corresponding social networks during the information spreading process. In the second part, by applying the simulation result from gSEIR as the prediction of information spreading in the given social network, we test the effectiveness and efficiency of all provided algorithms to solve the influence maximization problem with cost limit. The result show that the heuristic algorithm HILR is the best for the IMLC problem.     

Consensus software: robustness and social good

In this column I explore some far-reaching issues of software development that lie at the intersection of robust software and sociopolitical systems. These two areas might seem unrelated-and most software developers would likely be horrified to have politics intrude on their programming efforts-but the intersection occurs through these premises: software systems administer and control much of our societal infrastructure; people would appreciate and better accept that control if they had input into the nature of the control and the systems' behavior; designers can make software systems more robust through redundancy, in which different versions of software components might cover for each other's mistakes and limitations; and if many people could contribute software to societal control systems, the systems might be more robust and better represent people's interests. The need for redundancy and the need for widespread participation can be mutually satisfying.     

Inferring social network user profiles using a partial social graph

Classification problems refer to the assignment of alternatives to predefined categories. In this work we focus on ordered classification, called sorting, in which the predefined categories indicate several degrees of interest or suitability of alternatives for a certain user. The assignment of alternatives is based on multiple conflicting criteria. This multi-criteria sorting approach is specially interesting for recommender systems aimed at finding the most suitable alternatives for each user. First, we study the ELECTRE-TRI-B sorting method, which follows the outranking approach based on comparing the evaluations of alternatives with the profile limits separating the categories. The complexity of some recommenders systems requires the extension of the classical ELECTRE-TRI-B method to manage a taxonomical organization of the set of criteria. In this paper we consider a set of criteria in the form of a hierarchy. The intermediate criteria in such a hierarchy correspond to different aspects of the recommendation procedure, such as content, context or cost. At each of these criteria, a sorting problem must be solved. Therefore, we propose extending ELECTRE-TRI-B to handle assignments of alternatives on several levels of the hierarchy. A hierarchical procedure for sorting is proposed, called ELECTRE-TRI-B-H. Secondly, the paper explains the integration of ELECTRE-TRI-B-H into a recommender system of touristic activities related to wine, called GoEno-Tur. This system is developed for the region of Tarragona, Catalonia (Spain), which is a well-recognized area of wine and cava production.     

Promoting social network awareness: A social network monitoring system

To increase communication and collaboration opportunities, members of a community must be aware of the social networks that exist within that community. This paper describes a social network monitoring system – the KIWI system – that enables users to register their interactions and visualize their social networks. The system was implemented in a distributed research community and the results have shown that KIWI facilitates collecting information about social interactions. Furthermore, the visualization of the social networks, given as feedback, appeared to have a positive impact on the group, augmenting their social network awareness.     

Identifying and facilitating social interaction with a wearable wireless sensor network

We have designed a highly versatile badge system to facilitate a variety of interaction at large professional or social events and serve as a platform for conducting research into human dynamics. The badges are equipped with a large LED display, wireless infrared and radio frequency networking, and a host of sensors to collect data that we have used to develop features and algorithms aimed at classifying and predicting individual and group behavior. This paper overviews our badge system, describes the interactions and capabilities that it enabled for the wearers, and presents data collected over several large deployments. This data is analyzed to track and socially classify the attendees, predict their interest in other people and demonstration installations, profile the restlessness of a crowd in an auditorium, and otherwise track the evolution and dynamics of the events at which the badges were run.     

Examining online citizenship behaviours in social network sites: a social capital perspective

As users have flocked to social network sites (SNSs), these sites have gained tremendous scale and concomitant social influence. This growth has come at the cost of social disruption caused by the posting of abusive comments and rumours that turn out to be false. To combat these negative phenomena, this study proposes SNS citizenship behaviour and examines it from the perspective of social capital theory. This study further examines how the key characteristics of SNS in terms of the concept of customer value affect social capital development in an SNS context. The test results explain that the structural, relational, and cognitive dimensions of social capital have significant direct and indirect effects on the SNS citizenship behaviour. These findings also explain that four key characteristics (exploration, communication support, playfulness, and responsiveness) of SNS affect the three dimensions of social capital. This study contributes to the literature in its establishment of the concept of SNS citizenship behaviour and examines it from the social capital theory perspective. Its findings have practical implications through its guidance on how to develop SNS features and manage these sites for the citizenship behaviour of their users, which are achievements for the harmonious and effective functioning of SNS.     

On social computing research collaboration patterns: a social network perspective

The field of social computing emerged more than ten years ago. During the last decade, researchers from a variety of disciplines have been closely collaborating to boost the growth of social computing research. This paper aims at identifying key researchers and institutions, and examining the collaboration patterns in the field. We employ co-authorship network analysis at different levels to study the bibliographic information of 6 543 publications in social computing from 1998 to 2011. This paper gives a snapshot of the current research in social computing and can provide an initial guidance to new researchers in social computing.     

Recommendations based on a heterogeneous spatio-temporal social network

Recommender systems in location-based social networks (LBSNs), such as Facebook Places and Foursquare, have focused on recommending friends or locations to registered users by combining information derived from explicit (i.e. friendship network) and implicit (i.e. user-item rating network, user-location network, etc.) sub-networks. However, previous models were static and failed to adequately capture user time-varying preferences. In this paper, we provide a novel recommendation method based on the time dimension as well. We construct a hybrid tripartite (i.e., user, location, session) graph, which incorporates 7 different unipartite and bipartite graphs. Then, we test it with an extended version of the Random Walk with Restart (RWR) algorithm, which randomly walks through the network by using paths of 7 differently weighted edge types (i.e., user-location, user-session, user-user, etc.). We evaluate experimentally our method and compare it against three state-of-the-art algorithms on two real-life datasets; we show a significant prevalence of our method over its competitors.     

Visualizing the evolution of a web-based social network

Weblogs are dynamic websites updated via easy-to-use content management systems and organized as a set of chronologically ordered stories, frequently built around a link or including links to other weblogs. Since they are managed by individuals, their links tend to mirror or, in some cases, establish new types of social relations, thereby creating a social network. Studying the evolution of this network allows the discovery of emerging social structures and their growth trends. In this paper, we demonstrate the advantages of using the self-organizing maps (SOM) to visualize the evolution of a social network formed by a set of blogs, from their beginning to their current state. By observing the position a weblog is mapped to, it is easy to see what communities it belongs to nowadays, and how and when it became a part of those communities. The proposed procedure gives some insight on how communities are formed and have evolved. In this study, we apply this method to Blogalia, a blog-hosting site from which we have obtained a complete set of data and, by using SOM projections, we have drawn some conclusions on what drives the evolution of its implicit social network.     

Using structural information for distributed recommendation in a social network

Social networks are social structures that depict relational structure of different entities. The most important entities are usually located in strategic locations within the network. Users from such positions play important roles in spreading the information. The purpose of this research is to make a connection between, information related to structural positions of entities and individuals advice selection criteria in a friendship or trust network. We explore a technique used to consider both frequency of interactions and social influence of the users. We show, in our model, that individual positions within a network structure can be treated as a useful source of information in a recommendation exchange process. We then implement our model as a trust-based exchange mechanism in NetLogo, which is a multi-agent programmable modeling environment. The experimental results demonstrate that structural position of entities can indeed retain significant information about the whole network. Utilizing social influence of entities leads to an increase in overall utility of the system.     

Crowdsourcing social network service for social enterprise innovation

Social enterprises (SE) have developed various innovative business models to accomplish both social and business values for years. Not few SE’s persons have observed some critical and urgent social issues as well as human problems, but just few SE’s social media or crowdsourcing services can be provided people an efficient way to interact for these social problems. Thus, the research is to solve the need which is an integrative platform of social media and crowdsourcing service to support business innovation of social enterprise. Such a SE-specific crowdsourcing social network service (SNS) facilitate people can obtain the shared resources to further achieve the projects (i.e, volunteer activities) or even start up an innovative SE (i.e., lumni, 20%+, The Big Issue). In order to achieve SE’s charitable objective, this research is to propose and design an integration of social network and crowdsourcing service – HIVE that can provide people to post ideas, share resources, or even attend the crowdsourcing projects by the different resources. Thus, HIVE can give users to seek business opportunities, collect, connect, and users’ data analysis for social and human resources. HIVE even can provide several ways to facilitate human and resource corporations to support SE’s business innovation based on six pre-defined areas of SE and deal with the critical matching process.     

CyVOD: a novel trinity multimedia social network scheme

How to comprehensively explore, improve and deploy multimedia social networks (MSNs) has become a hot topic in the era of emerging pervasive mobile multimedia. More and more MSNs offer a great number of convenient tools, services, and applications for multimedia contents including video and audio that users share willingly and on demand. However, concerns with digital rights management (DRM)-oriented multimedia security, as well as the efficiency of multimedia usage and sharing are meanwhile intensified due to easier distribution and reproduction of multimedia content in a wide range of social networks. The paper proposes a comprehensive framework for multimedia social network, and realized a cross-platform MSN prototype system, named as CyVOD, to support two kinds of DRM modes. The proposed framework effectively protects copyrighted multimedia contents against piracy, and supports a more efficient recommendation system for its better handling of the tradeoff between multimedia security and ease of use.     

A model of a trust-based recommendation system on a social network

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents.     

Factors mediating disclosure in social network sites

In this paper, we explore how privacy settings and privacy policy consumption (reading the privacy policy) affect the relationship between privacy attitudes and disclosure behaviors. We present results from a survey completed by 122 users of Facebook regarding their information disclosure practices and their attitudes about privacy. Based on our data, we develop and evaluate a model for understanding factors that affect how privacy attitudes influence disclosure and discuss implications for social network sites. Our analysis shows that the relationship between privacy attitudes and certain types of disclosures (those furthering contact) are controlled by privacy policy consumption and privacy behaviors. This provides evidence that social network sites could help mitigate concerns about disclosure by providing transparent privacy policies and privacy controls.     

Minimizing the expected complete influence time of a social network

Complete influence time specifies how long it takes to influence all individuals in a social network, which plays an important role in many real-life applications. In this paper, we study the problem of minimizing the expected complete influence time of social networks. We propose the incremental chance model to characterize the diffusion of influence, which is progressive and able to achieve complete influence. Theoretical properties of the expected complete influence time under the incremental chance model are presented. In order to trade off between optimality and complexity, we design a framework of greedy algorithms. Finally, we carry out experiments to show the effectiveness of the proposed algorithms.     

Extracting influential nodes on a social network for information diffusion

We address the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models. The past study showed that a greedy strategy can give a good approximate solution to the problem. However, a conventional greedy method faces a computational problem. We propose a method of efficiently finding a good approximate solution to the problem under the greedy algorithm on the basis of bond percolation and graph theory, and compare the proposed method with the conventional method in terms of computational complexity in order to theoretically evaluate its effectiveness. The results show that the proposed method is expected to achieve a great reduction in computational cost. We further experimentally demonstrate that the proposed method is much more efficient than the conventional method using large-scale real-world networks including blog networks.