Sharif Digital Repository

A class of networks are those with both positive and negative links. In this manuscript, we studied the interplay between positive and negative ties on mesoscopic level of these networks, i.e., their community structure. A community is considered as a tightly interconnected group of actors; therefore, it does not borrow any assumption from balance theory and merely uses the well-known assumption in the community detection literature. We found that if one detects the communities based on only positive relations (by ignoring the negative ones), the majority of negative relations are already placed between the communities. In other words, negative ties do not have a major role in community... 

This paper studies the relationship between the position of individual scientists within co-authorship networks and their scientific performance. Using co-authorship data from African scientists in the Health and Medical Sciences within a timespan of 15 years (2000–2015), we characterize the collaboration networks and calculate centrality measures for each scientist to explore how scientific production and impact can be associated with their position within the network. Our findings reveal that authors who occupy a better position within their network and are deemed to actively collaborate with others also have a higher research impact. In this regard, South African scientists do not differ... 

Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users’ movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic-decaying kernel for the time of check-ins and a... 

We consider a market in which two competing sellers offer two similar products on a social network. In this market, each agent chooses iteratively between the products based on her neighbors reactions and prices. This introduces two games: one between the agents and one between the sellers. We show that the first game is a full potential game and provide an algorithm to compute its convergence point. We also study various properties of the second game such as its equilibrium points and convergence  

Modeling is one of the major research areas in social network analysis whose goal is to study networks structure and its evolution. Motivated by the intuition that members in social networks behave selfishly, network creation games have been introduced for modeling social networks. In this paper, our aim is to measure how much the output graphs of a given network creation game are compatible with a social network. We first show that the precise measurement is not possible in polynomial time. Then we propose a method for its approximation; finally, we show the usability of our method by conducting experiments on real network data  

With the widespread access of people to the Internet and the increasing usage of social networks in all nations, social networks have become a new source to study cultural similarities and differences. We identified major issues in traditional methods of data collection in cross-cultural studies: Difficulty in access to people from many nations, limited number of samples, negative effects of translation, positive self-enhancement illusion, and a few unreported problems. These issues are either causing difficulty to perform a cross-cultural study or have negative impacts on the validity of the final results. In this paper, we propose a framework that aims to calculate cultural distance among... 

Online social networks (OSNs) can be considered as huge success. However, this success costs users their privacy and loosing ownership of their own data; Sometimes the operators of social networking sites, have some business incentives adverse to users’ expectations of privacy. These sort of privacy breaches have inspired research toward privacy- preserving alternatives for social networking in a decentralized fashion. Yet almost all alternatives lack proper feasibility and efficiency, which is because of a huge mismatch between aforementioned goal and today’s network’s means of achieving it. Current Internet architecture is showing signs of age. Among a variety of proposed directions for a... 

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a... 

Social media platforms thrive upon the intertwined combination of user-created content and social interaction between these users. In this paper, we aim to understand what early user activity patterns fuel an ultimately durable user lifespan. We do so by analyzing what behavior causes potentially durable contributors to abandon their "social career"at an early stage, despite a strong start. We use a uniquely processed temporal dataset of over 6 billion Reddit user interactions on covering over 14 years, which we make available together with this paper. The temporal data allows us to assess both user content creation activity and the way in which this content is perceived. We do so in three... 

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts-Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given... 

Twitter as one of the most important microblogging online social networks has attracted more than 200 million users in recent years. Although there have been several attempts on characterizing the Twitter by using incomplete sampled data, they have not been very successful to estimate the characteristics of the whole network. In this paper, we characterize Twitter by sampling from its social graph and user behaviors through a random walk based sampling technique called Respondent-Driven Sampling (RDS). To the best of our knowledge, for the first time RDS method and its estimator are used in order to obtain uniform unbiased estimation of several key structural and behavioral properties of... 

Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and hasmany applications, such as friendship recommendation and identifyingmalicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based... 

In recent years, with the rapid development of online social networks, an enormous amount of information has been generated and diffused by human interactions through online social networks. The availability of information diffused by users of online social networks has facilitated the investigation of information diffusion and influence maximization. In this paper, we focus on the influence maximization problem in social networks, which refers to the identification of a small subset of target nodes for maximizing the spread of influence under a given diffusion model. We first propose a learning automaton-based algorithm for solving the minimum positive influence dominating set (MPIDS)... 

Social norms are a core concept in social sciences and play a critical role in regulating a society's behavior. Organizations and even governmental bodies use this social component to tackle varying challenges in the society, as it is a less costly alternative to establishing new laws and regulations. Social networks are an important and effective infrastructure in which social norms can evolve. Therefore, there is a need for theoretical models for studying the spread of social norms in social networks. In this paper, by using the intrinsic properties of norms, we redefine and tune the Rescorla-Wagner conditioning model in order to obtain an effective model for the spread of social norms. We... 

In recent years, with the rapid development of online social networks, an enormous amount of information has been generated and diffused by human interactions through online social networks. The availability of information diffused by users of online social networks has facilitated the investigation of information diffusion and influence maximization. In this paper, we focus on the influence maximization problem in social networks, which refers to the identification of a small subset of target nodes for maximizing the spread of influence under a given diffusion model. We first propose a learning automaton-based algorithm for solving the minimum positive influence dominating set (MPIDS)... 

This paper investigates the performance of epidemic routing, in mobile social networks (MSNs), which makes use of the store-carry-forward paradigm for communication. Real-life mobility traces show that people have skewed location visiting preferences, with some places visited frequently and some others infrequently. In order to model epidemic routing in MSNs, we first analyze the time taken for a node to meet the first node belonging to a set of nodes restricted to move in a specific subarea. Afterwards, a monolithic stochastic reward net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting... 

Differentiating between real and fake news propagation through online social networks is an important issue in many applications. The time gap between the news release time and detection of its label is a significant step towards broadcasting the real information and avoiding the fake. Therefore, one of the challenging tasks in this area is to identify fake and real news in early stages of propagation. However, there is a tradeoff between minimizing the time gap and maximizing accuracy. Despite recent efforts in detection of fake news, there has been no significant work that explicitly incorporates early detection in its model. The proposed method utilizes recurrent neural networks with a...