HiNGE: enabling temporal network analytics at scale

  • Authors:

  • Udayan Khurana;Amol Deshpande

  • Affiliations:

  • University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA

  • Venue:
  • Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
  • Year:
  • 2013

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Abstract

However, much of the prior work on those topics has been restricted to static networks, a primary reason being the lack of efficient temporal data management systems to store and query large dynamic network datasets. In this demonstration proposal, we present HiNGE (Historical Network/Graph Explorer), a system that enables interactive exploration and analytics over large evolving networks through visualization and node-centric metric computations. HiNGE is built on top of a distributed graph database system that stores the entire history of a network, and enables efficiently retrieving and analyzing multiple graph snapshots from arbitrary time points in the past. The cornerstone of our system is a novel hierarchical parallelizable index structure, called DeltaGraph, that enables compact recording of the historical trace of a network on disk, and supports efficient retrieval of historical snapshots for single-site or parallel processing. The other key component of our system is an in-memory graph data structure, called GraphPool, that can maintain hundreds of historical graph snapshots in main memory in a non-redundant manner. We demonstrate the efficient and usability of our system at performing temporal analytics over large-scale dynamic networks.