Amir Sheikhahmadi | Searcharxiv

arXiv2021

A Composite Centrality Measure for Improved Identification of Influential Users

In recent years, the problem of identifying the spreading ability and ranking social network users according to their influence has attracted a lot of attention; different approaches have been proposed for this purpose. Most of these approaches rely on the topological location of nodes and their neighbours in the graph to provide a measure that estimates the spreading ability of users. One of the most well-known measures is k-shell; additional measures have been proposed based on it. However, as the same k-shell index may be assigned to nodes with different degrees, this measure suffers from low accuracy. This paper is trying to improve this by proposing a composite centrality measure in that it combines both the degree and k-shell index of nodes. Experimental results and evaluations of the proposed measure on various real and artificial networks show that the proposed measure outperforms other state-of-the-art measures regarding monotonicity and accuracy.

cs.SI↗

arXiv2015

Improving detection of influential nodes in complex networks

Recently an increasing amount of research is devoted to the question of how the most influential nodes (seeds) can be found effectively in a complex network. There are a number of measures proposed for this purpose, for instance, high-degree centrality measure reflects the importance of the network topology and has a reasonable runtime performance to find a set of nodes with highest degree, but they do not have a satisfactory dissemination potentiality in the network due to having many common neighbors ($\mbox{CN}^{(1)}$) and common neighbors of neighbors ($\mbox{CN}^{(2)}$). This flaw holds in other measures as well. In this paper, we compare high-degree centrality measure with other well-known measures using ten datasets in order to find a proportion for the common seeds in the seed sets obtained by them. We, thereof, propose an improved high-degree centrality measure (named DegreeDistance) and improve it to enhance accuracy in two phases, FIDD and SIDD, by putting a threshold on the number of common neighbors of already-selected seed nodes and a non-seed node which is under investigation to be selected as a seed as well as considering the influence score of seed nodes directly or through their common neighbors over the non-seed node. To evaluate the accuracy and runtime performance of DegreeDistance, FIDD, and SIDD, they are applied to eight large-scale networks and it finally turns out that SIDD dramatically outperforms other well-known measures and evinces comparatively more accurate performance in identifying the most influential nodes.

cs.SI↗