754,427 research outputs found

    Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks

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    Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of human's physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (facebook, coauthor and email social networks), we find that the excitable senor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods

    Outward Influence and Cascade Size Estimation in Billion-scale Networks

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    Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes SS will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence, and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log⁥4n)\Omega(\log^4 n) times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201

    Online Influence Maximization in Non-Stationary Social Networks

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    Social networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample products; in this way, the advertiser hopes to increase the popularity of the product in the users' friend circles by the world-of-mouth effect, and thus maximizes the number of users that information of the production can reach. There has been a body of literature studying the influence maximization problem. Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology. In practice, the social network topology and the influence probabilities are typically unknown to the advertiser, which can be varying over time, i.e., in cases of newly established, strengthened or weakened social ties. In this paper, we focus on a dynamic non-stationary social network and design a randomized algorithm, RSB, based on multi-armed bandit optimization, to maximize influence propagation over time. The algorithm produces a sequence of online decisions and calibrates its explore-exploit strategy utilizing outcomes of previous decisions. It is rigorously proven to achieve an upper-bounded regret in reward and applicable to large-scale social networks. Practical effectiveness of the algorithm is evaluated using both synthetic and real-world datasets, which demonstrates that our algorithm outperforms previous stationary methods under non-stationary conditions.Comment: 10 pages. To appear in IEEE/ACM IWQoS 2016. Full versio

    Identifying influencers from sampled social networks

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    Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10%–30% of the networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks

    WHAT AFFECTS THE ADVERTISING SHARING BEHAVIOR AMONG MOBILE SNS USERS? THE RELATIONSHIPS BETWEEN SOCIAL CAPITAL, OUTCOME EXPECTATIONS AND PREVENTION PRIDE

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    Mobile networks practice of social networking service that gives individuals an easy way to exchange messages and ideas with others base on interpersonal relationships. However, why individuals spread advertisements in their social circles through mobile applications is not well understood: is this the result of environment impact or the result of individual characteristics? To tackle this problem, we apply social capital theory to examine how social capital influence advertising recommendation quality and advertising sharing behavior in mobile networks. And, we also use social cognitive theory and regulatory focus theory to investigate the motivations behind people\u27s advertising sharing behavioral in mobile networks. Data collected from 319 mobile social networking users provide support for the proposed model. The analysis of the sample shows that the social capital and outcome expectations are significant indicators of individual’s ad-sharing behavior in the mobile SNS environment. Moreover, the prevention pride has an obvious interaction influence on the perception and behavior of M-ad sharing. Implications for research and practice are discussed

    The Tacit Knowledge Problem in Multinational Corporations: Japanese and US Offshore Knowledge Incubators

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    This paper examines the ‘cognitive’ and ‘societal’ aspects of the tacit knowledge transfer problem in MNCs. Based on a comparative analysis of the overseas R&D labs of US and Japanese MNCs in the UK, it examines how home-based models of learning influence MNCs’ transnational social spaces for learning and their capabilities to address the tacit knowing problem. It illustrates how the US professional ‘networks of practice’ (NoP) and the Japanese organizational ‘communities of practice’ (CoP) approaches to transnational learning unfold in practice. It also examines how divergence between home and host country institutions governing knowledge production inhibits cross-societal tacit knowing.comparative thinking; tacit knowledge; knowledge transfer in MNCs; innovation and R&D; organizational learning; communities of practice

    Social influence in networks of practice: An analysis of organizational communication content

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    Networks of Practice (NoPs) facilitate knowledge sharing among geographically dispersed organization members. This research tests whether social influence in NoPs is reinforced by actors' embeddedness in practice (knowledge about informal content), organizational embeddedness (knowledge about formal organizational content), structural embeddedness (knowledge about who knows what), and relational embeddedness (knowledge about informal relationships). A full-fledged automated content analysis on all postings on four NoPs maintained by a multinational chemical company revealed four dimensions in communication content that largely coincide with the proposed embeddedness types. We measured social influence by assessing to what extent actors' use of uncommon language traits was adopted in the responses to the postings. Hypothesis testing revealed that network members who communicate about informal practice, and know who knows what, exert more social influence than others. The results suggest that network members' social influence is rooted in their utilitarian value for others, and not in their organizational or relational embeddedness. © The Author(s) 2011

    Entrepreneurship. Context matters: Matters in context

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    Context has been recognized as shaping entrepreneurship. However, much less has been said about the interplay between entrepreneurship and social context and how entrepreneurship may actually shape context. Through looking at current and ongoing work, this presentation characterizes entrepreneurship as a contextual event. In doing so, it draws on the ideas of embeddedness, social capital, social bonds and social networks to show that relationships play meaningful roles in the entrepreneurial process. Insight is presented about the role of such relationships and how those that exist between entrepreneurs and the communities with whom they engage can influence practice and outcomes. The argument is also made that entrepreneurship is both shaped and influenced by context, and that it is therefore critical to look at entrepreneurial matters in their context.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂ­a Tech
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