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Leaders in social networks, the Delicious case.

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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.

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Citations
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Journal ArticleDOI

Vital nodes identification in complex networks

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

David F. Gleich
- 06 Aug 2015 - 
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

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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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI

The anatomy of a large-scale hypertextual Web search engine

TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Proceedings Article

The PageRank Citation Ranking : Bringing Order to the Web

TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
Journal Article

The Anatomy of a Large-Scale Hypertextual Web Search Engine.

Sergey Brin, +1 more
- 01 Jan 1998 - 
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Journal ArticleDOI

Authoritative sources in a hyperlinked environment

TL;DR: This work proposes and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure, and has connections to the eigenvectors of certain matrices associated with the link graph.
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