Memory Efficient Edge Addition Designs for Large and Dynamic Social Networks

Abstract

The availability of large volumes of social network data from a variety of social and socio-technical networks has greatly increased. These networks provide critical insights into understanding various domains including business, healthcare, and disaster management. The relationships and interactions between different entities represented in most of these data sources are constantly evolving. Graph processing and analysis methodologies that can effectively integrate data changes while minimizing recomputations are needed to handle these dynamic networks. In addition, the size of these information sources is constantly increasing, therefore we need designs that can perform analysis that are memory efficient in order to address resource constraints. In this paper, we show how our anytime anywhere framework can be used to construct memory-efficient closeness centrality algorithms. In particular, we will show how dynamic edge additions can be efficiently handled in the proposed scheme.