The Markov Chain Simulation Method for Generating Connected Power Law Random Graphs

Graph models for real-world complex networks such as the Internet, the WWW and biological networks are necessary for analytic and simulation-based studies of network protocols, algorithms, engineering and evolution. To date, all available data for such networks suggest heavy tailed statistics, most notably on the degrees of the underlying graphs. A practical way to generate network topologies that meet the observed data is the following degree-driven approach: First predict the degrees of the graph by extrapolation from the available data, and then construct a graph meeting the degree sequence and additional constraints, such as connectivity and randomness. Within the networking community, this is currently accepted as the most successful approach for modeling the inter-domain topology of the Internet. In this paper we propose a Markov chain simulation approach for generating a random connected graph with a given degree sequence. We introduce a novel heuristic to speed up the simulati...

• Type: Conference
• Year: 2003
• Where: ALENEX
• Authors: Christos Gkantsidis, Milena Mihail, Ellen W. Zegura
• Keywords: ALENEX 2003, ALENEX 2007, Available Data, Degree Sequences, Real-world Complex Networks