Artificial Bee Colony-Based Influence Maximization Approach for Social Coding Portal GitHub

Abstract

Influence maximization is the main source of virality of any social media post/marketing activity. In recent trends, influence maximization has moved towards analytic approach instead of just being a suggestive metaphor for various social media paradigm. In this article, ego-centric approach and a bio-inspired algorithm is applied on social coding community, Github, for influence maximization. First, developers' and projects' egocentric network-based studies are conducted to find out influential developer and project based on varying social media measures. Second, artificial bee colony (ABC) bio-inspired algorithm is used to select social bees (i.e., developers set and projects set in rapid convergence towards an optimal solution to achieve influence maximization). Algorithm result ensures the best solution in terms of social community connectivity path optimization in less run time while finding most influential social bees.

Initial Clustering Based on the Swarm Intelligence Algorithm for Computing a Data Density Parameter

“…e fuzzy C-clustering algorithm belongs to the objective function-based method and shows good performance when working with large amounts of data. erefore, the object of this study is the little-known C cluster algorithm [16].…”