An Axiomatic and an Average-Case Analysis of Algorithms and Heuristics for Metric Properties of Graphs

Understand

In recent years, researchers proposed several algorithms that compute metric quantities of real-world complex networks, and that are very efficient in practice, although there is no worst-case guarantee.

• In this work, we propose an axiomatic framework to analyze the performances of these algorithms, by proving that they are efficient on the class of graphs satisfying certain axioms.
• Furthermore, we prove that the axioms are verified asymptotically almost surely by several probabilistic models that generate power law random graphs, such as the In recent years, researchers proposed several algorithms that compute metric quantities of real-world complex networks, and that are very efficient in practice, although there is no worst-case guarantee.
• In this work, we propose an axiomatic framework to analyze the performances of these algorithms, by proving that they are efficient on the class of graphs satisfying certain properties.