الفهرس 
AcknowledgmentOnly 14 pages are availabe for public view
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Abstract
Counting number of triangles in the graph is considered a major task in many large scale graph analytics problems such as clustering coefficient, transitivity ratio, trusses, etc. In recent years, MapReduce becomes one of the most popular and powerful framework for analyzing large scale graphs in clusters of machines. In this thesis, we propose two new algorithms based on graph partitioning using MapReduce. The two algorithms avoid the problem of duplicate counting triangles that other algorithms suffer from Rectangles for bipartite graphs are like triangles for unipartite graphs as both represent the smallest cycles in such graphs. Rectangle Counting is considered an important task in many bipartite network analysis metrics and is considered the core of computing such metrics, especially in cluster coefficient, bitruss, etc. However, there are few efficient algorithms to deal with this problem, especially in a large bipartite graph. In this thesis, we use MapReduce to enhance and develop algorithms to count rectangles in a large bipartite graph. The experimental results conducted for the proposed triangle counting algorithms show high efficiency of the two algorithms in comparison with an existing algorithm. The results show that the two algorithms overcome the existing algorithm in the execution time performance, especially in very large scale graphs. In the other hand, the experimental results conducted for the proposed rectangle counting algorithms show that our proposed MapReduce-based algorithms give a better execution time than the existing algorithms, especially when it is applied in very large bipartite graphs.