Combat network link prediction based on embedding learning

doi:10.23919/JSEE.2022.000036

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (2): 345-353.doi: 10.23919/JSEE.2022.000036

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Combat network link prediction based on embedding learning

Jianbin SUN(), Jichao LI*(), Yaqian YOU(), Jiang JIANG(), Bingfeng Ge()

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received:2020-11-11 Accepted:2022-03-01 Online:2022-05-06 Published:2022-05-06
Contact: Jichao LI E-mail:sunjianbin@nudt.edu.cn;ljc@hotmail.com;youyaqian13@nudt.edu.cn;jiangjiangnudt@nudt.edu.cn;bingfengge@ nudt.edu.cn
About author:|SUN Jianbin was born in 1989. He received his B.E. degree in management engineering, M.E. and Ph.D. degrees in management science and engineering from National University of Defense Technology, Changsha, Hunan, China, in 2012, 2014 and 2018, respectively. He is a Ph.D. and a lecturer in National University of Defense Technology. He was a visiting scholar with the Lab for Information Retrieval and Knowledge Management, School of Information Technology, York University, Toronto, ON, Canada. His research interests include system of systems engineering management and decision analysis under uncertainty. E-mail: sunjianbin@nudt.edu.cn||LI Jichao was born in 1990. He received his B.E. degree in management science, M.E. and Ph.D. degrees in management science and engineering from National University of Defense Technology, Changsha, Hunan, China, in 2013, 2015, and 2019, respectively. He is a Ph.D. and a lecturer in National University of Defense Technology. In 2017-2019, he was a visiting predoctoral fellow at the Northwestern Institute on Complex Systems (NICO) and Kellogg School of Management at Northwestern University, USA. His research interests focus on studying complex systems with a combination of theoretical tool and data analysis, including mathematical modeling of heterogeneous information networks, applying network methodologies to analyze the development of complex system-of-systems, and data-driven studying of the collective behavior of humans. E-mail: ljc@hotmail.com||YOU Yaqian was born in 1994. She received her B.E. and M.E. degrees in management science and engineering from National University of Defense Technology, Changsha, Hunan, China, in 2017 and 2019, respectively. She is a Ph.D. student in National University of Defense Technology. She is currently working in management science and engineering. Her research interests include the decision analysis under uncertainty and evidential reasoning. E-mail: youyaqian13@nudt.edu.cn||JIANG Jiang was born in 1981. He received his B.E. degree in systems engineering, M.E. and Ph.D. degrees in management science and engineering from National University of Defense Technology, Changsha, Hunan, China, in 2004, 2006, and 2011, respectively. He is currently an associate professor in National University of Defense Technology. He was a visiting scholar at Harvard University, Boston, MA, USA from 2018 to 2019. His research interests include evidential reasoning, uncertainty decision-making, and risk analysis. E-mail: jiangjiangnudt@nudt.edu.cn||GE Bingfeng was born in 1983. He received his B.E. degree in systems engineering, M.E. and Ph.D. degrees in management science and engineering from the National University of Defense Technology, Changsha, Hunan, China, in 2006, 2008, and 2014, respectively. He is currently an associate professor of management science and engineering at National University of Defense Technology. He was a visiting scholar with the Conflict Analysis Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada. His research interests include system-of-systems architecting and engineering management, portfolio decision analysis and conflict resolution. He is a technical committee member of Conflict Resolution of the IEEE Systems, Man, and Cybernetics Society, and a member of the IEEE Internet of Things Technical Community and the International Council on Systems Engineering. E-mail: bingfengge@ nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China(71901212; 71971213)

Abstract

Abstract:

Link prediction of combat networks is of significant military value for precisely identifying the vital infrastructure of the enemy target and optimizing the operational plan of our side. Due to the profound uncertainty in the battleground circumstances, the acquired topological information of the opponent combat network always presents sparse characteristics. To solve this problem, a novel approach named network embedding based combat network link prediction (NECLP) is put forward to predict missing links of sparse combat networks. First, node embedding techniques are presented to preserve as much information of the combat network as possible using a low-dimensional space. Then, we put forward a solution algorithm to predict links between combat networks based on node embedding similarity. Last, massive experiments are carried out on a real-world combat network case to verify the validity and practicality of the proposed NECLP. This paper compares six baseline methods, and experimental results show that the NECLP has outstanding performance and substantially outperforms the baseline methods.

Key words: link prediction, node embedding, combat networks, sparse feature

Jianbin SUN, Jichao LI, Yaqian YOU, Jiang JIANG, Bingfeng Ge. Combat network link prediction based on embedding learning[J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 345-353.

Figures/Tables 5

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Method		Evaluation metric
Method		AUC	Recall	Precision
Proposed method	NECLP-node2vec	0.754 ± 0.010	0.535 ± 0.019	0.007 ± 0.000
Proposed method	NECLP-DeepWalk	0.750 ± 0.011	0.518 ± 0.016	0.006 ± 0.000
Baseline method	CN	0.631 ± 0.028	0.389 ± 0.042	0.005 ± 0.000
	JAC	0.616 ± 0.023	0.312 ± 0.032	0.004 ± 0.000
	AA	0.659 ± 0.028	0.387 ± 0.042	0.005 ± 0.000
	PA	0.518 ± 0.043	0.259 ± 0.052	0.004 ± 0.000
	RA	0.672 ± 0.026	0.439 ± 0.037	0.006 ± 0.000
	WIC	0.631 ± 0.028	0.389 ± 0.042	0.005 ± 0.000

Combat network link prediction based on embedding learning

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