Dealing with the Best Attachment Problem via Heuristics

Ordering nodes by rank is a benchmark used in several contexts, from recommendation-based trust networks to e-commerce, search engines and websites ranking. In these scenarios, the node rank depends on the set of links the node establishes, hence it becomes important to choose appropriately the nodes to connect to. The problem of finding which nodes to connect to in order to achieve the best possible rank is known as the best attachment problem. Since in the general case the best attachment problem is NP-hard, in this work we propose heuristics that produce near-optimal results while being computable in polynomial time; simulations on different networks show that our proposals preserve both effectiveness and feasibility in obtaining the best rank.

[1]  Carl D. Meyer,et al.  Deeper Inside PageRank , 2004, Internet Math..

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

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

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

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

[6]  Pavel Berkhin,et al.  A Survey on PageRank Computing , 2005, Internet Math..

[7]  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.

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

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

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

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

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

[13]  Thorsten Joachims,et al.  In Google We Trust: Users' Decisions on Rank, Position, and Relevance , 2007, J. Comput. Mediat. Commun..

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

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

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

[17]  Franco Scarselli,et al.  Inside PageRank , 2005, TOIT.

[18]  Junaid Khan,et al.  Web Page Ranking Using Machine Learning Approach , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

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

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

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

[22]  Alexander V. Nazin,et al.  Adaptive randomized algorithm for finding eigenvector of stochastic matrix with application to PageRank , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.