Climbing Ranking Position via Long-Distance Backlinks

Abstract

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 NlogN. 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.

Marco Grassia

Assistant Professor · Network Science and Machine Learning

Assistant Professor. Researching Network Science and Geometric Deep Learning. University of Catania, Italy