Leaders in Social Networks, the Delicious Case

By Linyuan Lu, Yi-Cheng Zhang, C. Yeung, Tao Zhou. PLoS ONE, 2011

746 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/693ba6bce4de68e27ffd3f0136207de59e618ed8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Sophie Ahrens
2. Duanbing Chen
3. Linyuan Lu
4. Qiang Dong
5. Jian-Xiong Zhang
6. Tao Zhou
7. Zhi Zhao

In Statistical and Nonlinear Physics

1. Sophie Ahrens
2. Tao Zhou
3. Linyuan Lu
4. Duanbing Chen
5. Qiang Dong
6. Jian-Xiong Zhang
7. Zhi Zhao
8. Hui Gao

Citations in the corpus

• Recommender Systems (2012, methodology): “Besides web pages, similar self-consistent equations are also successfully applied in ranking people [307], genes [308], and so on.”
• Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering (2013, methodology): “The representative methods include the well-known HITs [17] and PageRank [18], as well as some recently proposed algorithms like LeaderRank [7,19] and TwitterRank [20].”
• Identifying a set of influential spreaders in complex networks (2016, methodology): “Random-walk based methods such as well-known PageRank[13] and LeaderRank[14] have been attracted a great deal of attention and indeed have been shown significant value in last few years.”