Human beings have been looking for models and methods to organize, dir=ltr Bipartite graph has a specific application and importance among the variety of presenting methods since the majority of systems in this field, such as recommender systems naturally model bipartite. Most of bipartite networks tend to cluster one side of graph behavior in order to recognize communications and interactions between members of that side and actually discover similar members. One-mode projection technique is an appropriate method for that purpose which recently has come to attention broadly in different areas such as Social Networks, Health Care systems, medicine and treatment, etc. Generally, some part of primary information of main bipartite graph will be missed under the projection. Hence, scientists have been trying to provide a method to determine the appropriate weight for yield projected edges in order to minimize information loss. On the other hand, performance level on big data is an important challenge within these methods. The majority of investigated databases in the field of bipartite network projection are huge, consequently, executing projection procedure takes lots of times. Therefore, we need methods which have acceptable speed as they keep accuracy and precision in projection. The following research aims to improve the existed algorithm speed by introducing a scalable method based on resource allocation for bipartite network projection, and we try to provide the appropriate speed while preserving precision through transferring the needed operations on a distributed infrastructure like Hadoop. Moreover, as a case study, we evaluate the performance of presented scalable algorithm in the field of Social Network which results to a lesser projection operation time in comparison to the undistributed mode. Also, we compared our proposed method with collaborative filtering method, a well known algorithm in recommendation field and as a result our method had higher execution speed overall. With using the biggest dataset, orkut, the proposed method has higher speed than the scalable CF by 33%. Then, we evaluate the scalability of the introduced method by a scalability metric named Speedup, which showed good scalability. Investigation accompanied with analyzing the execution time increasing in different states and configurations based on input data size growth. Keywords Bipartite Networks, Projection, Recommendation, Scalable
