Climbing Ranking Position via Long-Distance Backlinks

The best attachment consists in finding a good strategy that allows a node inside a network to achieve a high rank. This is an open issue due to its intrinsic computational complexity and to the giant dimension of the involved networks. The ranking of a node has an important impact both in economics and structural term e.g., a higher rank could leverage the number of contacts or the trusting of the node. This paper presents a heuristics aiming at finding a good solution whose complexity is \(N\log {N}\). The results show that better rank improvement comes by acquiring long distance in-links whilst human intuition would suggest to select neighbours. The paper discusses the algorithm and simulation on random and scale-free networks.

[1]  Vincenza Carchiolo,et al.  Network size and topology impact on trust-based ranking , 2017, Int. J. Bio Inspired Comput..

[2]  Xin Liu.  Towards Context-Aware Social Recommendation via Trust Networks , 2013, WISE.

[3]  Konstantin Avrachenkov,et al.  The Effect of New Links on Google Pagerank , 2006 .

[4]  Matthew K. O. Lee,et al.  EC-Trust (Trust in Electronic Commerce): Exploring the Antecedent Factors , 1999 .

[5]  Marcin Sydow.  Can One Out-Link Change Your PageRank? , 2005, AWIC.

[6]  Shlomo Moran,et al.  SALSA: the stochastic approach for link-structure analysis , 2001, TOIS.

[7]  Vicente P. Guerrero-Bote,et al.  A further step forward in measuring journals' scientific prestige: The SJR2 indicator , 2012, J. Informetrics.

[8]  Stéphane Gaubert,et al.  Ergodic Control and Polyhedral Approaches to PageRank Optimization , 2010, IEEE Transactions on Automatic Control.

[9]  Pu-Jen Cheng,et al.  Improving Ranking Consistency for Web Search by Leveraging a Knowledge Base and Search Logs , 2015, CIKM.

[10]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[11]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[12]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[13]  Clara Simón de Blas,et al.  Combined social networks and data envelopment analysis for ranking , 2018, Eur. J. Oper. Res..

[14]  Vincenza Carchiolo,et al.  Trust assessment: a personalized, distributed, and secure approach , 2012, Concurr. Comput. Pract. Exp..

[15]  David M. Pennock,et al.  Winners don't take all: Characterizing the competition for links on the web , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Chunyan Miao,et al.  Trust-based agent community for collaborative recommendation , 2006, AAMAS '06.

[17]  Vincenza Carchiolo,et al.  The Cost of Trust in the Dynamics of Best Attachment , 2015, Comput. Informatics.

[18]  Vincenza Carchiolo,et al.  Gain the Best Reputation in Trust Networks , 2011, IDC.

[19]  Vincenza Carchiolo,et al.  Dealing with the Best Attachment Problem via Heuristics , 2016, IDC.

[20]  Vincenza Carchiolo,et al.  Direct trust assignment using social reputation and aging , 2017, J. Ambient Intell. Humaniz. Comput..

[21]  Martin Olsen,et al.  An approximation algorithm for the link building problem , 2012, ArXiv.

[22]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[23]  Vincenza Carchiolo,et al.  Users' attachment in trust networks: reputation vs. effort , 2013, Int. J. Bio Inspired Comput..

[24]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[25]  Hector Garcia-Molina,et al.  The Eigentrust algorithm for reputation management in P2P networks , 2003, WWW '03.

[26]  Vincenza Carchiolo,et al.  A Heuristic to Explore Trust Networks Dynamics , 2013, IDC.