Leaders in social networks, the Delicious case.
Linyuan Lü,Yi-Cheng Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Chi Ho Yeung,Chi Ho Yeung,Chi Ho Yeung,Tao Zhou,Tao Zhou,Tao Zhou +9 more
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TLDR
It is shown that LeaderRank outperforms PageRank in terms of ranking effectiveness, as well as robustness against manipulations and noisy data, which suggest that leaders who are aware of their clout may reinforce the development of social networks, and thus the power of collective search.Abstract:
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.read more
Citations
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Vital nodes identification in complex networks
Linyuan Lü,Linyuan Lü,Duanbing Chen,Xiao-Long Ren,Qian-Ming Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Tao Zhou +7 more
TL;DR: In this paper, the state-of-the-art algorithms for vital node identification in real networks are reviewed and compared, and extensive empirical analyses are provided to compare well-known methods on disparate real networks.
Journal ArticleDOI
Identifying influential nodes in complex networks
TL;DR: Simulations on four real networks show that the proposed semi-local centrality measure can well identify influential nodes and is a tradeoff between the low-relevant degree centrality and other time-consuming measures.
Journal ArticleDOI
Recommender Systems
TL;DR: In this article, a review of recent developments in recommender systems and discuss the major challenges and major challenges of recommender system and their potential impacts and future directions are discussed, and they compare and evaluate available algorithms and examine their roles in the future developments.
Journal ArticleDOI
PageRank Beyond the Web
TL;DR: Google's PageRank method was developed to evaluate the importance of web-pages via their link structure and apply to any graph or network in any domain this paper, however, the mathematics of PageRank are entirely general and can be used to evaluate any graph and any network.
Journal ArticleDOI
Vital nodes identification in complex networks
Linyuan Lü,Linyuan Lü,Duanbing Chen,Xiao-Long Ren,Qian-Ming Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Tao Zhou +7 more
TL;DR: This review clarifies the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art, and provides extensive empirical analyses to compare well-known methods on disparate real networks and highlight the future directions.
References
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