Efficient generation of large random networks.

By V. Batagelj, U. Brandes. Physical review. E, Statistical, nonlinear, and soft matter physics, 2005

304 citations recorded by the source corpus.

Random networks are frequently generated, for example, to investigate the effects of model parameters on network properties or to test the performance of algorithms. Recent interest in the statistics of large-scale networks sparked a growing demand for network generators that can generate large numbers of large networks quickly. We here present simple and efficient algorithms to randomly generate networks according to the most commonly used models. Their running time and space requirement is linear in the size of the network generated, and they are easily implemented.

Subfields: Mathematical Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4fa49861404325afca8a8f0decb64a323934ec22

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mathematical Physics

1. Yadati Narahari
2. Ramasuri Narayanam

In Statistical and Nonlinear Physics

1. Yadati Narahari
2. Ramasuri Narayanam
3. Kyomin Jung
4. Wooram Heo

Citations in the corpus

• A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks (2011, methodology): “We efficiently generate sparse random graphs with 500 nodes using the algorithm presented by Batagelj and Brandes [4].”