Sharif Digital Repository

Recently complex networks, have been widely used as a model to study the behavior of human brain. By affecting different parts the brain, Neuronal disorders change the structure of functional brain networks. Investigating these changes using different graph theory metrics can be a useful methodology for human brain analysis in health and disease. Alzheimer's disease (AD) is a neural disease causing impairment in different brain activities including memory and cognition.The aim of this study is to construct the functional brain network of 17 AD patients and 17 healthy control subjects at resting state condition and analyzing them using the theory of complex networks in order to achieve a... 

Complex networks serve as generic models for many social, technological and biological systems that have been shown to share a number of common structural properties such as power-law degree distribution and small-worldness. These networks determine interactions and influence the spread of diseases, behaviours and ideas. Epidemics and failures like new behaviours ideas and innovations can spread throughout a network and prevent it from doing its functionalities. In this work, we investigate error and attack tolerance of epidemic spreading in complex networks. The main content of this thesis is arranged in three sections as follows: Firstly, the effect of random and systematic failures is... 

Journals and conferences in computer science are the major venue for publishing new achievement in the field. It is an expert opinion that a number of top conferences in computer science are even more important than journals. In this work we aim at studying this in terms of citation analysis. To this end, we took 100 top journals and 63 top conferences and extracted their citation graph through Scopus dataset. We then constructed the citation graph in which the nodes were the journals and conferences and the links corresponded to the citations of the papers. We used various measures to rank the nodes in the graph. The ranking methods included Prestige, PageRank, Eigenfactor, HITS and SALSA.... 

Complex networks science have received tremendous attention in recent years and the brain is one of the systems to which graph theoretical tools have been applied. Alzheimer’s disease (AD) is a neurodegenerative disease affecting many of elderly population. AD changes the anatomy of the brain, which subsequently results in changes in its functions. These changes have been frequently reported in signals recorded from the brain (such as MEG, fMRI and EEG). Among these neuroimaging techniques EEG is one of the most aproprate methods for extracting functional connectivites according to high temporal resolution. In this thesis, we aimed at analyzing the properties of EEG-based functional networks... 

Iran is regarded as a dry land and has suffered from extreme to severe drought conditions in recent years. Drought – which is mainly caused by shortage in rainfall – affects the normal life in Iran. Development of tools for effectively monitoring and predicting drought intensity might help the policy makers to reduce the vulnerability of the areas affected by drought. In this thesis, we showed that the intensity of drought can be predicted using satellite imagery data and recurrent neural networks. To this end, the standardized precipitation index (SPI) was chosen as an index for drought and normalized deviation of vegetation index (NDVI) as a remote sensing measure extracted from NOAA-AVHRR... 

The modern science of networks helps us to have a better understanding of complex systems. Networked systems can be found everywhere and many systems can be represented by a complex network. A networked structure consists of a number of nodes and links connecting to them. Networks’ ability in information propagation is one of their amazing features that have attracted lots of scholars to work on. It has potential applications in many fields ranging from marketing to biology, epidemiology and sociology. Information propagation studies how information such as computer viruses, contagion, rumor, or new product’s interest propagates over a network. Percolation theory and various epidemic models... 

In recent years, there have been increasing attempts to study brain connectivity. Among a number of brain mapping techniques, Electroencephalography is an easy to use and cheap method that can be used in the study of brain function. One way of understanding the intricate wiring pattern and functions of brain is to consider it as a complex network. In this approach, a graph of brain functions, based on the functional relation of recorded electric signals, is constructed and then the network is evaluated with a number of network metrics that measure its different aspect of structure. Different neurological and psychological diseases can affect the connectivity power within the brain; as a... 

Opinion formation is one of the major topics in social networks analysis and mining. A number of methods have been introduced for describing and simulating this phenomenon. In this thesis, we proposed a novel optimization framework based on an opinion formation model that used the Deffuant’s model with some changes in neighbors’ selection policy. Its efficiency was compared with a number of benchmark optimization methods including genetic algorithms, differential evolution and particle swarm optimization.The proposed model demonstrated better performance than the others.In the proposed, each person changes his opinion based on one of his neighbors who has the best performance in terms of the... 

The World Wide Web has been experiencing a massive growth regarding its content and users in recent years; therefore the need for effective means of accessing and processing available items has attracted a wide range of researchers and industries. Recommender systems has emerged to help both users to find what they may be interested in and the producers to sell their products more efficiently. As the number of these techniques grow, the need to evaluate them properly increases as well. However the proposed evaluation metrics are very diverse and often inconsistent with each other. Although there had been immense research in this field, there is no united and proper approach for evaluation of... 

Network structures have many applications in everyday life. For example, we can refer to the real world systems in which clients are indicated by nodes and edges represent their connectivity. Network science has witnessed tremendous progress over the last decade. The field of Network Science and Engineering started by exploring properties of real-world network systems and constructing proper models. Previous studies shows that all real world systems share some common structural features, such as scale free degree distribution and group structure. Analyzing interaction between network structure and its dynamic is a one of the main problem in studying complex dynamical networks. In this study,... 

Information cascade or affection in a broad social network is introduced as a dynamic epidemic phenomenon in the society. As people notify a new innovation, technology, or hobby, they try to share it with their friends, colleges or neighbors. Till now most of the cascade models are presented for unsigned network, in which all links have the same sign (such as friends and trusted networks). In these networks cascade is independent of the edge sign. But in reality signed networks are as common as simple networks. Thus, in this thesis, we study information cascade in networks with positive and negative edges. We link the cascade size to community structure of signed networks; communities are... 

Trust prediction, the ability to identify how much to trust to allocate an unknown user, is an important prerequisite toward the development of scalable on-line e-commerce communities. We are more likely to purchase an item from a seller on an e-commerce websites such as eBay or Amazon, if our trusted acquaintances have reported positive experiences with that seller in the past. Reviews from trusted users will carry more weight towards the purchasing decision than reviews from anonymous or unknown customers. Thus, these platforms must support computational mechanisms for propagating trust between users. One of the significant challenges in the trust prediction problem is the unprecedented... 

Recommender systems are in the center of network science and are becoming increasingly important in individual business for providing efficient personalized services and products to users. The focus of previous research in the field of recommendation systems were on improving the accuracy of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation list as key characteristics of modern recommender systems. In many cases, novelty and precision do not go in the same direction and the accuracy-novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in... 

Mankind with it’s finite resources (Time, Energy, …) cannot make use of every accessible option in daily activities (such as buying items, listening to music and reading news), and is restricted to decide on a handful of them. Available options are increasing on a daily basis and these surplus of available options had an adverse effect; Thus, leading us to more baffling situations. As a result, need for external assistance appeared in decision situations. Considering exceptional computation power available to computers, a framework named Recommender Systems were developed. Recommender systems try to use their accessible data in order to make fitting suggestions to users. Personalization and... 

With the over-increasing growth of the information provided for the web users, from content providing systems and web stores to social networks, the exictence of recommender systems is strongly needed. Recommender systems personalize web for the users and help them with finding relevent information in the huge era of World Wide Web. Collaborative filtering methods are known as the most successful and vastly used recommendation systems. Although they generally outperform content-based algorithms, in cold-start situation and especially in the presence of the new items, they fail to predict ratings for the new items or make good recommendations. This problem is not negligible in the systems... 

Recommender systems are in the center of network science and becoming increasingly important in individual businesses for providing efficient personalized services and products to users. The focus of previous research in the field of recommendation systems was on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation list as key characteristics of modern recommender systems. In many cases, novelty and precision do not go in the same direction and the accuracy-novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a... 

In this thesis a method based on opinion formation in complex networks aiming to solve unconstrained optimization problem has been studied. Unconstrained optimization problem is the problem of searching for the best solution in the solution space. Optimization problems have many applications in various fields that due to advances in data storage and also appearance of new big data applications solving these problems in large scales gained much importance. Different methods have been proposed in order to solve such problems but because of high computational complexity, many of them do not show good performance. A category of methods called stochastic search methods are used to overcome the... 

In the recent years, the problem of modeling a cognitive task using phase oscillators has been receiving a significant attention. In this view, single neurons are no longer elementary computational units. Rather, coherent oscillating groups of neurons are seen as nodes of networks performing cognitive tasks. From this assumption, we develop a model of stimulus-response learning and recognition. The most significant part of our work is defining learning methods for natural frequencies and coupling weights in a coupled phase oscillator network under Kuramoto conditions. In this thesis, we improved the previous models by not only emphasizing on the frequency of the oscillators but also taking... 

Regarding the analysis of real-world networks, the ability to detect regions of high density or “community” is of great importance in network science. The motivation behind this endeavor comes from many publications confirming that the agglomeration of relations is meaningful on its own. At the same time, there have been some attempts, although less than the unsigned route, toward the detection of community in groups of positive relations (similar to unsigned mode) that have, internally, as few negative ties as possible. As an example, signed Modularity is one of the few algorithms in this route.In this thesis, we first tried to analyze the structure of real networks to find out about the... 

For along time, analysis of financial markets is an interesting topic for mankind. There are different statistical methods to analyze financial time series. In this manuscript, financial markets are analyzed from network view point. Inference of hidden structures between companies of a market is merit information so we introduce different methods to infer
hidden network structure of financial markets. In addition, we will show that dynamic of network structure can provide important information about external sources of influence on financial markets. Actually, we show affect of political events on financial market of Iran. At last, we generalize diffusion concept at financial networks...