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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 43327 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Rabiee, Hamid Reza
- Abstract:
- Communication among people over the emerging networks has been the focus of attention in different branches of science during last decades. Online Social Networks (OSNs), with more than hundreds of millions of users are powerful means for directing information within and across societies. Thus, studying various aspects of OSNs is an important issue for researchers. Due to large number of users and friendship relationships among them, gathering complete information from an OSN is not feasible. On the other hand, hiding users information and crawlers limitations are challenges for gathering complete data. A Common solution for this problem is Sampling from OSNs. Sampling from OSNs (and networks, in general) have two main categories: with replacement and without replacement. Since the second category represents topological characteristics better, in this thesis we have focused on the second category. It is important to note that the methods in the second category are mostly biased unless their bias is corrected through a mathematical procedure. Recent works have proposed a new approach for correcting bias of sampling methods which corresponds to sampling from a network based on a model, called ’Model Based Sampling’. In this thesis, we propose a representative model for OSNs and correct the BFS sampling bias based on this model. To this end, we chose the LFR model after analyzing available network models. Moreover, the required modification has been proposed for the LFR base model. The proposed model was applied to both real and simulated graphs. Our results shows at least 14 percent and at most 100 percent improvement in estimating network parameters compared to the base method which is based on the Random Graph model which cannot estimate some parameters at all.
- Keywords:
- Data Gathering ; Network Model ; Random Walk ; Online Social Networks ; Graph Traversal
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