An Efficient Parallel Method for Optimizing Concurrent Operations on Social Networks

This paper presents our approach to optimize the performance of both reading and writing concurrent operations on large-scale social networks. Here, we focus on the directed and unweighted relationships among members in a social network. It can then be illustrated as a directed, unweighted graph. Moreover, determining the relationship between any two members is similar to finding the shortest path between two vertices. With such a large-scale dynamic social network, we face the problem of having concurrent operations from adding or removing edges dynamically while one may ask to determine the relationship between two members. To solve this issue, we propose an efficient parallel method based on (i) utilizing an appropriate data structure, (ii) parallelizing the updating actions and (iii) improving the performance of query processing by both reducing the searching space and computing in multi-threaded parallel. Our method was validated by the datasets from SigMod Contest 2016 and SNAP DataSet Collections with fine positive experimental results compared to other solutions.