This paper is an overview of studying the solar features in a complex network approach. First, we introduce the structural features of complex networks and important network parameters. Applying the detrended fluctuation and rescaled range analysis and nodes degree power-law distributions confirmed the non-randomness of the solar features complex networks. Using the HEALPix pixelization and considering all parts of the solar surface under the same conditions, as well as applying centrality parameters (the nodes with the highest connectivity, closeness, betweenness, and Pagerank) showed that the active areas on the solar surface were correctly identified and were consistent with observations. A review of the complex structure of the solar proton flux and active regions also showed that in these networks, the average clustering coefficient and Page-rank parameters are suitable criteria to use in event prediction methods. The complex network of sunspots has also shown that sunspots and sunspot groups are formed through complex nonlinear dynamics.

In every country, airports are among the most important air transport systems in that country. When an aircraft flies from one airport to another, it creates a graph that can be completed with information about each flight, such as the number of flights per path, the number of passengers, traffic load, and so on. In the present paper, the airports of Iran and the domestic flights are considered as a network and the structure of the network is analyzed, and then the measures of complex networks such as degree distribution, shortest path length, analysis of centralities, clustering coefficient and their correlation and the way these centralities behave are examined. This analysis shows the Iranian Airport network (IAN) that has a degree distribution described by the power function. The average path length in this network is 1. 9, and the average clustering coefficient is 0. 69, which meets the characteristics of a small-world network and is also considered an example of a disassortative network. The purpose of this research is to investigate the network of airports in Iran, which is ultimately important for the expansion of airports, and also to identify the important points of airports.

In this study, an image retrieval system is proposed based on complex network model. Assuming a prior image categorization, firstly, a multilayered complex network is constructed between the images of each category according to the color, texture, and shape features. Secondly, by defining a meta-path as the way of connecting two images in the network, a set of informative meta-paths are composed to find the similar images by exploring the network. The established complex network provides an efficient way to benefit from the image correlations to enhance the similarity search of the images. On the other hand, employing diverse meta-paths with different semantics leads to measuring the image similarities based on effective image features for each category. The primary results indicate the efficiency and validity of the proposed approach.

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 specifi c features for analysing and visual-izing 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 [17]. Then we define some functional indicators including: Basic Functionalities, Graph type Support, File For-mats Support, Indicator Supports, Visualization Layouts Support, and Community Detection Support. In the next step, by using analytic hierarchical processing (AHP) and truly de nable 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.