Analysing and Predicting the Runtime of Social Graphs

The explosion of Social Network Analysis (SNA) in many different areas and the growing need for powerful data analysis has emphasized the importance of in-memory big data processing in computer systems. Particularly, large-scale graphs are gaining much more attention due to their wide range of application. This rise, accompanied by a massive number of vertices and edges, led computations to become increasingly expensive and time consuming. That is why there is a move towards distributed systems or Big Data cluster(s) to provide the required computational power and memory to handle such demand of huge graphs. Thus, figuring out whether a new social graph dataset can be processed successfully on a personal machine or there is a need for a distributed system or big- memory machine is still a remaining open question. In this paper, we try to address this question by providing a comparative analysis for the performance of two of the most well known SNA tools for performing commonly used graph algorithms such as counting Triads, calculating Degree Distribution and finding Clusters which can give an indication of the possibility of carrying out the work on a personal machine. Based on these measurements, we train different supervised machine learning models for predicting the execution time of these algorithms. We compare the accuracy of the different machine learning models and provided the details of the most accurate model that can be exploited by end users to better estimate the execution time expected for processing new social graphs on a personal machine