Abstract
As we all know, decision-makers need the high quality battlefield information to design the best operation plans, however, real intelligence data are often incomplete and noisy, where missing links prediction methods and spurious links identification algorithms can be applied. Military organizations could be modeled as heterogeneous complex networks, where nodes represent different types of functional units and edges denote different types of communication links. In this paper, we proposed a combined link prediction index considering both the nodes’ types effects and their structural similarities, and demonstrated that it is remarkably superior to all the 25 existing similarity-based methods both in predicting missing links and identifying spurious links in a real military network data; we also investigated the algorithms’ robustness under noisy environment, and showed our method maintained the best performance under the condition of small noise. In the end, as the FINC-E model, here used to describe the heterogeneous military organizations, is also suitable to many other social organizations, such as criminal networks, business organizations, etc., thus our method has its prospects in these areas for many tasks, like detecting the underground relationships between terrorists, predicting the potential business markets for decision-makers, and so on.
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Acknowledgements
The authors are grateful to the anonymous referees for their insightful suggestions and comments. This research was supported by the National Basic Research Program of China under Grant No. 71471176, No. 71471174 and No. 61303266. All authors acknowledge the National University of Defense Technology.
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Fan, C., Liu, Z., Xiu, B., Yu, L. (2016). Missing and Spurious Interactions in Heterogeneous Military Networks. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds) Social Media Processing. SMP 2016. Communications in Computer and Information Science, vol 669. Springer, Singapore. https://doi.org/10.1007/978-981-10-2993-6_2
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DOI: https://doi.org/10.1007/978-981-10-2993-6_2
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