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      Leaders in Social Networks, the Delicious Case


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          Finding pertinent information is not limited to search engines. Online communities can amplify the influence of a small number of power users for the benefit of all other users. Users' information foraging in depth and breadth can be greatly enhanced by choosing suitable leaders. For instance in delicious.com, users subscribe to leaders' collection which lead to a deeper and wider reach not achievable with search engines. To consolidate such collective search, it is essential to utilize the leadership topology and identify influential users. Google's PageRank, as a successful search algorithm in the World Wide Web, turns out to be less effective in networks of people. We thus devise an adaptive and parameter-free algorithm, the LeaderRank, to quantify user influence. We show that LeaderRank outperforms PageRank in terms of ranking effectiveness, as well as robustness against manipulations and noisy data. These results suggest that leaders who are aware of their clout may reinforce the development of social networks, and thus the power of collective search.

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          Most cited references23

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          Assortative Mixing in Networks

          M. Newman (2002)
          A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.
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            Network Data and Measurement

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              Missing and spurious interactions and the reconstruction of complex networks.

              Network analysis is currently used in a myriad of contexts, from identifying potential drug targets to predicting the spread of epidemics and designing vaccination strategies and from finding friends to uncovering criminal activity. Despite the promise of the network approach, the reliability of network data is a source of great concern in all fields where complex networks are studied. Here, we present a general mathematical and computational framework to deal with the problem of data reliability in complex networks. In particular, we are able to reliably identify both missing and spurious interactions in noisy network observations. Remarkably, our approach also enables us to obtain, from those noisy observations, network reconstructions that yield estimates of the true network properties that are more accurate than those provided by the observations themselves. Our approach has the potential to guide experiments, to better characterize network data sets, and to drive new discoveries.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                27 June 2011
                : 6
                : 6
                : e21202
                [1 ]Research Center for Complex System Science, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
                [2 ]Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
                [3 ]Department of Physics, University of Fribourg, Chemin du Musée 3, Fribourg, Switzerland
                Universita' del Piemonte Orientale, Italy
                Author notes

                Conceived and designed the experiments: LL Y-CZ CHY TZ. Performed the experiments: LL. Analyzed the data: LL Y-CZ CHY TZ. Contributed reagents/materials/analysis tools: LL. Wrote the paper: Y-CZ CHY.

                Lü et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                : 16 March 2011
                : 23 May 2011
                Page count
                Pages: 9
                Research Article
                Interdisciplinary Physics
                Statistical Mechanics
                Social and Behavioral Sciences
                Information Science



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