Performance Evaluation of Social Network Analysis Algorithms Using Distributed And In-Memory Computing Environments
Author(s):
ELVIN JOHN V , M. S. Ramaiah Institute of TechnologyKeywords:
Social Network Analysis, Distributed Computing, in-Memory Computing, Apache SparkAbstract:
Analysis on Social Networking sites such as Facebook, Flickr and Twitter has long been a trending topic of fascination for data analysts, researchers and enthusiasts in the recent years to maximize the value of knowledge acquired from processing and analysis of the data. Apache Spark is an Open-source data-parallel computation engine that offers faster solutions compared to traditional Map-Reduce engines such as Apache Hadoop. This paper discusses the performance evaluation of Apache Spark for analyzing social network data. The performance of analysis varies significantly based on the algorithms being implemented. This is the reason to what makes this analysis worthwhile of evaluation with respect to their versatility and diverse nature in the dynamic field of Social Network Analysis. We compare performance of Apache Spark by evaluating the performance using various algorithms (PageRank, Connected Components, Counting Triangle, K-Means and Cosine Similarity) making efficient use of the Spark cluster.
Other Details:
| Manuscript Id | : | J4RV2I3026 |
| Published in | : | Volume : 2, Issue : 3 |
| Publication Date | : | 01/06/2016 |
| Page(s) | : | 127-134 |
