Abstract
Inferring the structure of an unknown network is a difficult problem of interest to researchers, academics , and industrialists . We develop a novel algorithm to infer nodes and edges in an unknown network. Our algorithm utilizes monitors that detect incident edges and adjacent nodes with their labels and degrees. The algorithm infers the network through a preferential random walk with a probabilistic restart at a previously discovered but unmonitored node, or a random teleportation to an unexplored node. Our algorithm outperforms random walk inference and random placement of monitors inference in edge discovery in all test cases. Our algorithm outperforms both methodologies in node inference in synthetic test networks; on real networks it outperforms them in the beginning of the inference. Finally, a website was created where these algorithms can be tested live on preloaded networks or custom networks as desired by the user. The visualization also displays the network as it is being inferred, and provides other statistics about the real and inferred networks.
Keywords
- Monitor Placement
- Restart Probability
- Illary Lymph Node
- Random Walker Prefers
- Unknown Network
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgments
The authors would like to thank the DoD for partially sponsoring the current research. We would also like to thank and acknowledge the Naval Postgraduate School’s Center for Educational Design, Development, and Distribution (CED3) for creating the live visualization [4] for this project.
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Davis, B., Gera, R., Lazzaro, G., Lim, B.Y., Rye, E.C. (2016). The Marginal Benefit of Monitor Placement on Networks. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_7
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DOI: https://doi.org/10.1007/978-3-319-30569-1_7
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