Systematic topology analysis and generation using degree correlations

Researchers have proposed a variety of metrics to measure important graph properties, for instance, in social, biological, and computer networks. Values for a particular graph metric may capture a graph’s resilience to failure or its routing efficiency. Knowledge of appropriate metric values may influence the engineering of future topologies, repair strategies in the face of failure, and understanding of fundamental properties of existing networks. Unfortunately, there are typically no algorithms to generate graphs matching one or more proposed metrics and there is little understanding of the relationships among individual metrics or their applicability to different settings. We present a new, systematic approach for analyzing network topologies. We first introduce the dK-series of probability distributions specifying all degree correlations within d-sized subgraphs of a given graph G. Increasing values of d capture progressively more properties of G at the cost of more complex r...

• Type: Conference
• Year: 2006
• Where: SIGCOMM
• Authors: Priya Mahadevan, Dmitri V. Krioukov, Kevin R. Fall, Amin Vahdat
• Keywords: Future Topologies, Important Graph Properties, Probability Distribution, SIGCOMM 2006