TAGs: scalable threshold-based algorithms for proximity computation in graphs

  • Authors:
  • A. Lyritsis;A. N. Papadopoulos;Y. Manolopoulos

  • Affiliations:
  • Aristotle University, Thessaloniki, Greece;Aristotle University, Thessaloniki, Greece;Aristotle University, Thessaloniki, Greece

  • Venue:
  • Proceedings of the 14th International Conference on Extending Database Technology
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

A fundamental and very useful operation in graphs is the computation of the proximity between nodes, i.e., the degree of dissimilarity (or similarity) between two nodes v and u. This is an important tool both in graph databases and graph mining applications, because it provides the base to support more complex tasks such as graph partitioning, clustering, classification, to name a few. All methods proposed in the literature assume that proximity is computed on a single graph by using a single distance measure. In addition, most of them focus on the proximity between node pairs. In this work, we present for the first time, scalable algorithms that: (i) they support proximity computation in multiple graph instances, (ii) they enable the utilization of several distance measures, (iii) they support proximity queries around a source node without limiting to node pairs and (iv) they support extensions for metric-based and skyline query processing. The main result of our work is the design of Threshold Algorithms for Graphs (denoted as TAGs), which are studied and evaluated experimentally by using real-life as well as synthetic graphs, based on both the G(n, p) Erdõs-Rényi model and power law degree distributions.