In the last decade, the problem of community detection in complex networks has attracted the attention of many researchers in many domains, several methods and algorithms have been proposed to deal with this problem, many of them consider it as an optimization problem and various bio-inspired algorithms have been applied to solve it. In this work, the authors propose a new method for community detection in complex networks using the Penguins Search Optimization Algorithm (PeSOA), the authors use the modularity density evaluation measure as a function to maximize and they propose also to enhance the algorithm by using a new initialization strategy. The proposed algorithm has been tested on four popular real-world networks; experimental results compared with other known algorithms show the effectiveness of using this method for community detection in social networks.
The GitHub website represents nowadays an essential tool for developers from around the world; it is considered as a social network for them in which they can share their open source projects with others in a form of repositories. This paper presents and discusses the design and the implementation of a new recommender system for GitHub repositories based on a collaborative-filtering approach, which can be useful in many ways in the process of searching for the right solutions to build projects. The GitHub website is becoming very popular these days, a lot of projects are shared by millions of developers, building this recommender system can reduce searching time and make search results more and more relevant. The authors evaluate their system by conducting a set of experiments on a real data set using different well-known metrics and the k-fold cross validation method. Results obtained from these experiments are very promising, the authors found that their recommender system can reaches better precision and recall accuracy.
Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.
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