Name
Abstract | ||
|---|---|---|
Developing models to generate realistic graphs of communication networks often requires a deep understanding and extensive analysis of the underlying network structure. Since deployed communication networks are dynamic, the findings a generator is based on might lose validity. We alleviate the need for extensive analysis of graphs by estimating parameters of a probabilistic model. The model parameters encode the structure of the graph, which is thus learned in an unsupervised fashion. Synthetic graphs can be generated from the model and will have the structure previously inferred. For this, we use the Stochastic-Block-Model (SBM) and the Degree-Corrected-Block-Model (DCBM), a variant allowing for heavy tailed degree distributions. The models originate in the social sciences and separate a graph into groups of nodes. To show the applicability of the models to the task of synthetic graph generation in the domain of communication networks, we use one router level and one IP-to-IP communication graph. We assert the quality of the generated models by evaluating a number of graph features and comparing our results to those obtained with the network generator Orbis. We find our approach to be on par with, or even outperforming Orbis. Furthermore, the models are able to capture large-scale structure in communication networks. |
Year | Venue | Field |
|---|---|---|
2017 | IM | Data mining,Telecommunications network,Computer science,Network topology,Theoretical computer science,Stochastic block model,Null model,Statistical model,Complex network,Router,Probabilistic logic |
DocType | Citations | PageRank |
Conference | 1 | 0.39 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Kalmbach | 1 | 28 | 6.12 |
| Andreas Blenk | 2 | 215 | 23.28 |
| Markus Klugel | 3 | 60 | 8.81 |
| Wolfgang Kellerer | 4 | 1474 | 157.92 |