Leaders in Social Networks, the Delicious Case

TL;DR

This paper introduces LeaderRank, a new algorithm to identify influential users in social networks, enhancing collective information seeking and outperforming PageRank in effectiveness and robustness.

Contribution

The paper proposes LeaderRank, an adaptive, parameter-free influence ranking algorithm tailored for social networks, improving upon PageRank for identifying key users.

Findings

LeaderRank outperforms PageRank in ranking effectiveness.

LeaderRank is more robust against manipulations and noisy data.

Influential leaders can strengthen social networks and 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.