New Approaches for Performance Optimization and Analysis of Large-Scale Dynamic Social Network Analysis using Anytime Anywhere Algorithms

During the last decade, the availability of large amounts of social network information from various social and socio-technical networks has increased dramatically. These data sources are inherently dynamic with constantly evolving relationships and connections between entities. Research in this area must address the challenge of analyzing these dynamic datasets under potentially strict time constraints. In addition, due to the sheer size of these networks, they tend to be stored and analyzed on distributed platforms. In our previous work, we designed methodologies which are anytime and anywhere to design scalable parallel/distributed algorithms that incorporate different forms of network changes. In this work, we will investigate various schemas to balance the incorporation of dynamic network changes that will substantially reduce idleness and load imbalances among processors. We will show theoretically that in most cases our buffer-based methodology performs better than the more common way of handling changes as they come in.