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.     

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.     

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.     

Co-occurrence prediction in a large location-based social network

Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to “check-in” the places (locations) when they visit them. The accurate geographical and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co-occurrences and social ties, and the results show that the co-occurrences are strongly correlative with the social ties. Second, we present a study of predicting two users whether or not they will meet (co-occur) at a place in a given future time, by exploring their check-in habits. In particular, we first introduce two new concepts, bag-of-location and bag-of-time-lag, to characterize user’s check-in habits. Based on such bag representations, we define a similarity metric called habits similarity to measure the similarity between two users’ check-in habits. Then we propose a machine learning formula for predicting co-occurrence based on the social ties and habits similarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.     

具有通信时延的离散时间二阶多个体网络的一致性问题

针对具有通信时延的离散时间二阶多个体系统的一致性问题,采用了具有静态领导者的一致性算法.根据广义Nyquist判据和Gerschgorin圆盘定理,得到了系统渐近收敛到领导者状态的充分条件.在个体与领导者构成的连接拓扑满足一定连通性的前提下,该充分条件是分散形式的,与控制参数、邻居个体之间的连接权值相关,而与通信时延大小无关.仿真结果证明了结论的正确性.     

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.     

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.     

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.     

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.     

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.     

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.     

Hybrid job offer recommender system in a social network

Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.     

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.     

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.     

Sequential seeding to optimize influence diffusion in a social network

The problem of node seeding for optimizing influence diffusion in a social network can be applied in many fields, and thus has drawn much attention. In real life, because of a variety of reasons, decision maker needs to make a sequence of decisions about how to select the seeded nodes. In this paper, we study the problem of sequentially seeding nodes in a social network such that the complete influence time is minimized. We formulate a Markov decision process to describe the problem and embed a modified greedy search method into an online algorithm to solve the Markov decision process. Numerical experiments are performed to show the effectiveness of the proposed online algorithm.     

基于Agent社会圈子网络的语言竞争模型

语言传播网络是一种典型的社会网络,语言网络的结构和动态性对语言的竞争与传播具有重要影响。鉴于此特点,提出以同一地区的语言竞争现象作为研究对象,利用基于Agent的社会圈子网络来构建更能接近实际的语言网络。社会网络中个体网络的结构参数和整体网络的参数都具有良好的社会网络特点。网络中的Agent可以被赋予不同大小的社会圈子,Agent可以在网络中移动、新生和死亡,进而引起社会网络旧关系的断开和新关系的建立。网络中的Agent的语言类型代表了单语X、单语Y和双语Z(单语X和单语Y)三类,语言的传播模型为横向传播和纵向传播。在分析了语言地位、吸引因子、横纵向传播的最高比例、语言的人口比例对语言竞争影响的基础上,重点分析了社会交往半径和社会流动性对语言竞争的影响。仿真结果表明,与静态社会网络模型相比,所提模型更加贴近实际社会,能够有效提高语言之间共存的可能性,为研究濒危语言的保存提供一个较好的环境。     

结合社交网络图的多模态虚假信息检测模型

针对现有虚假信息检测方法主要基于单模态数据分析,检测时忽视了信息之间相关性的问题,提出了结合社交网络图的多模态虚假信息检测模型。该模型使用预训练Transformer模型和图像描述模型分别从多角度提取各模态数据的语义,并通过融合信息传播过程中的社交网络图,在文本和图像模态中加入传播信息的特征,最后使用跨模态注意力机制分配各模态信息权重以进行虚假信息检测。在推特和微博两个真实数据集上进行对比实验,所提模型的虚假信息检测准确率稳定为约88%,高于EANN、PTCA等现有基线模型。实验结果表明所提模型能够有效融合多模态信息,从而提高虚假信息检测的准确率。     

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.