Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Bipartite structure of all complex networks
Information Processing Letters
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Regression Modeling Strategies
Regression Modeling Strategies
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
| Hi-index | 0.00 |
The purpose of this article is to present new undirected bigraph generator. Bigraphs (or bipartite graphs) contain nodes of two types and there exist edges only between nodes of different types. This data structure can be observed in various real-life scenarios. Random generator can be used to describe and better understand the scenarios. Moreover, the generator can output a wide range of synthetic datasets. We believe that the datasets can be utilized to evaluate performance of various algorithms that are deployed in such settings. The generative procedure is based on the preferential attachment principle. The principle is combined with the iterative growth mechanism and results in the power-law node degree distribution. Our algorithm extends the classic Barab$#225;si - Albert model. We obtain the same scaling exponent as in the classic model, when we set equal parameters for both modalities. However, when we abandon the symmetry we are able to build graphs with wider spectrum of scaling exponents.