Sharif Digital Repository

Nowadays, the world is based on the interaction between individuals, groups and different systems. The actual networks that have a complex structure and behavior are called complex networks. Complex networks are one of the new knowledge that studies the connections. The complex systems represented as graph, with non-trivial topological features—features that do not occur in simple networks.With the vast development of computer networks, complex networks appear in different categories such as social networks, citation networks, collaboration networks and communication networks. Data mining is the process of exploring hidden knowledge in data bases and it has applications in complex networks.... 

Complex Networks are everywhere, many complex systems can be represented as networks, such as the power grid, the road network, the airline network and the Protein-protein interaction network, delivery and distribution networks, and telephone networks. A fundamental issue concerning complex networks is the robustness of the overall system to the failure of its constituent parts. The robustness of networks against failure, targeted attacks to individuals’ components, and the impact on the performance of the system has become an important issue for practical reasons in the last few years. The failures attacks or errors on networks are not limited to the deletion of vertex, for common... 

Complex network studies grow by the scientific community, thanks to the increasing availability of real-world network data. The importance of using analysis on this type of network algorithms and community structure is one of the most important problems in the field of complex networks. Community Detection in a multidimensional network is still under investigation in the research community. The aim of this research is to give an algorithm for community discovery. We have reviewed the basis for multidimensional network analysis and will present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. Then, we... 

In the last decade, participation of hundreds of millions of users in social networks, has turned social networks into a powerful tool to steer the flow of information. As a result, the study of different aspects of these networks has become one of the indispensable interests of many researchers. One of the most important research problems in social network analysis is the community detection problem. From structural point of view, a community is a set of nodes with more internal links than external links, and from behavioral point of view, a community is a set of nodes with similar attributes and features. Most of previous works on community detection have focused on the structure of the... 

Nowadays, there exist many real networks with distinctive features in comparison with random networks. Social networks, collaboration networks, citation networks, protein networks and communication networks are some example of complex network classes. Nowadays these networks are widespread and have many applications and the study of complex networks is an important research area. In many applications, the “synthetic networks generation” is one of the first levels of complex networks analysis. This level has many applications such as simulation and extrapolation. Many generative models are proposed for complex network modeling in recent years. By the use of these models, synthetic networks... 

Markov Random Fields are suitable and applicable tools for modeling dependency of data dimensions; But since time complexity of parameter learning for these models is exponential with respect to data dimension size, density estimation using these models is restricted in action. In the other hands, with growing use of neural networks in many problems a class named autoregressive networks were applied for density estimation. Although the learning time of neural network parameters is not very low, there are many efforts for parameter learning acceleration. In this thesis Markov Random Fields structural properties are used in autoregressive networks. In our proposed method the hypothesis space...