Document Type : Research Paper
Department of Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
The analysis of complex networks become more popular through the easily access of huge network data resources in the last years. Researchers have developed techniques and models to help understanding and predicting the behaviour of complex network systems. This advanced analysis is not possible without proper softwares and tools. A large number of tools are available with specific features for analysing and visualizing network systems and we can use a software or a set of suitable tools based on these features and capabilities for the project. Understanding the features of tools and softwares help to achieve better results from network analysis. In this paper, first we review the structure of diﬀerent types of networks. Based on Wenjun paper, the complex networks are divided into four categories: information networks; social networks; Biological networks and Technological networks . Then we define some functional indicators including: Basic Functionalities, Graph type Support, File Formats Support, Indicator Supports, Visualization Layouts Support, and Community Detection Support. In the next step, by using analytic hierarchical processing (AHP) and truly definable criteria try to evaluate main complex network analysis (CNA) softwares. Eventually, an opportunity is provided using AHP to identify, understand, and evaluate completely four main CNA softwares objectively before identifying and selecting the most eﬃcient CNA software.