A geometric preferential attachment model of networks II

  • Authors:
  • Abraham D. Flaxman;Alan M. Frieze;Juan Vera

  • Affiliations:
  • Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA;Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA;Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
  • Year:
  • 2007

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Abstract

A detailed understanding of expansion in complex networks can greatly aid in the design and analysis of algorithms for a variety of important network tasks, including routing messages, ranking nodes, and compressing graphs. This has motivated several recent investigations of expansion properties in real-world graphs and also in random models of real-world graphs, like the preferential attachment graph. The results point to a gap between real-world observations and theoretical models. Some real-world graphs are expanders and others are not, but a graph generated by the preferential attachment model is an expander whp. We study a random graph Gn that combines certain aspects of geometric random graphs and preferential attachment graphs. This model yields a graph with power-law degree distribution where the expansion property depends on a tunable parameter of the model. The vertices of Gn are n sequentially generated points x1, x2, ..., xn chosen uniformly at random from the unit sphere in R3. After generating xt, we randomly connect it to m points from those points in x1, x2, ..., xt-1....