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| Authors: | Minas Gjoka, Maciej Kurant, Athina Markopoulou |
| Group: | Communication Systems |
| Type: | Inproceedings |
| Title: | 2.5K-Graphs: from Sampling to Generation |
| Year: | 2013 |
| Month: | April |
| Book Titel: | 32nd IEEE Conference on Computer Communications (INFOCOM 2013), Turin, Italy. |
| Abstract: | Understanding network structure and having access to realistic graphs plays a central role in computer and social networks research. In this paper, we propose a complete, practical methodology for generating graphs that resemble a real graph of interest. The metrics of the original topology we try match are the joint degree distribution (JDD) and the degree-dependent clustering coefficient ($ar{c}(k)$). We start by developing efficient estimators for these two metrics, based on a node sample collected via either independence sampling or random walks. Then, we process the output of the estimators to ensure that the target properties are realizable. Finally, we propose an efficient algorithm for generating topologies that have the exact target JDD and a $ar{c}(k)$ close to the target. Extensive simulations using real-life graphs show that the graphs generated by our methodology are similar to the original graph with respect to not only the two target metrics but also a wide range of other topological metrics; furthermore, our approach is order of magnitudes faster than state-of-the-art techniques. |
| Location: | Turin, Italy |
| Resources: | [BibTeX] |
