فهرست مطالب عسگرعلی بویر

In recent years, the sampling problem in massive graphs of social networks has attracted much attention for fast analyzing a small and good sample instead of a huge network. Many algorithms have been proposed for sampling of social network’ graph. The purpose of these algorithms is to create a sample that is approximately similar to the original network’s graph in terms of properties such as degree distribution, clustering coefficient, internal density and community structures, etc. There are various sampling methods such as random walk-based methods, methods based on the shortest path, graph partitioning-based algorithms, and etc. Each group of methods has its own pros and cones. The main drawback of these methods is the lack of attention to the high time complexity in making the sample graph and the quality of the obtained sample graph. In this paper, we propose a new sampling method by proposing a new equation based on the structural properties of social networks and combining it with bee colony algorithm. This sampling method uses an informed and non-random approach so that the generated samples are similar to the original network in terms of features such as network topological properties, degree distribution, internal density, and preserving the clustering coefficient and community structures. Due to the random nature of initial population generation in meta-heuristic sampling methods such as genetic algorithms and other evolutionary algorithms, in our proposed method, the idea of ​​consciously selecting nodes in producing the initial solutions is presented. In this method, based on the finding hub and semi-hub nodes as well as other important nodes such as core nodes, it is tried to maintain the presence of these important nodes in producing the initial solutions and the obtained samples as much as possible. This leads to obtain a high-quality final sample which is close to the quality of the main network. In this method, the obtained sample graph is well compatible with the main network and can preserve the main characteristics of the original network such as topology, the number of communities, and the large component of the original graph as much as possible in sample network. Non-random and conscious selection of nodes and their involvement in the initial steps of sample extraction have two important advantages in the proposed method. The first advantage is the stability of the new method in extracting high quality samples in each time. In other words, despite the random behavior of the bee algorithm, the obtained samples in the final phase mostly have close quality to each other. Another advantage of the proposed method is the satisfactory running time of the proposed algorithm in finding a new sample. In fact, perhaps the first question for asking is about time complexity and relatively slow convergence of the bee colony algorithm. In response, due to the conscious selection of important nodes and using them in the initial solutions, it generates high quality solutions for the bee colony algorithm in terms of fitness function calculation. The experimental results on real world networks show that the proposed method is the best to preserve the degree distribution parameters, clustering coefficient, and community structure in comparison to other method.

Discovering and analyzing of hidden information in complex networks and especially social networks has attracted the attention of many researchers. Community detection is a very important tool for understanding network information and its structure. Many approaches have been proposed, but proposing new methods with high accuracy and high quality are still one of the most exciting and challenging topics in community detection field. Label Propagation Algorithm (LPA) is one of the most popular algorithms in the field of community detection. The random nature and instability in the results of this algorithm make the LPA unreliable. Determining the core nodes and adopting a specific order of nodes based on their importance, more accurate diffusion of the core node label, and improving the way of selecting the correct label, are the main topics that we will discuss in this paper. In this paper, the proposed method which is combination of label diffusion of core nodes and label propagation method, has been able to improve the accuracy and efficiency of LPA by selecting and diffusing the core nodes label and improving the label update strategy. The results of experiments performed on standard and synthetic datasets and comparing the results show that the proposed algorithm performs better than the LPA and NIB-LPA algorithms.

Community structure is vital to discover the important structures and potential property of complex networks. In recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex network due to the advantages of linear time complexity and applicable for large-scale networks. However, there are many shortcomings in these methods such as instability, low accuracy, randomness, etc. The G-CN algorithm is one of local methods that uses the same label propagation as the LPA method, but unlike the LPA, only the labels of boundary nodes are updated at each iteration that reduces its execution time. However, it has resolution limit and low accuracy problem. To overcome these problems, this paper proposes an improved community detection method called SD-GCN which uses a hybrid node scoring and synchronous label updating of boundary nodes, along with disabling random label updating in initial updates. In the first phase, it updates the label of boundary nodes in a synchronous manner using the obtained score based on degree centrality and common neighbor measures. In addition, we defined a new method for merging communities in second phase which is faster than modularity-based methods. Extensive set of experiments are conducted to evaluate performance of the SD-GCN on small and large-scale real-world networks and artificial networks. These experiments verify significant improvement in the accuracy and stability of community detection approaches in parallel with shorter execution time in a linear time complexity.

Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social networks that maximize the influential spreading. Recently, there is a quick growing in studying on and diffusion. In recent years, several algorithms have been proposed for influence maximization problem. These studies include viral marketing, spreading of rumors, innovation, spread of diseases, etc. In this paper we present a new method for solving the for influence maximization problem that is called ICIM-GREEDY algorithm. In the proposed algorithm, we consider two important criteria that are not considered in previous work, one is strength of influence and another is sensitivity to influence. The proposed method is evaluated on standard datasets. The results show that the proposed method compared to other comparable algorithms has a better quality in finding the 50 influential nodes (seeds). Furthermore, ICIM-GREEDY often has better time complexity than other compared algorithms due to relatively fast convergence.