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

收稿日期:2020-11-11 接受日期:2022-03-01 出版日期:2022-05-06 发布日期:2022-05-06

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)

摘要/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

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 345-353.

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.

图/表 5

参考文献 39

1	CARES J R. An information age combat model. Proc. of the 9th International Command and Control Research and Technology Symposium on the Power of Information Age Concepts and Technologies, 2004.
2	LI J C, GE B F, ZHAO D L, et al Meta-path-based weapon-target recommendation in heterogeneous combat network. IEEE Systems Journal, 2019, 13 (4): 4433- 4441. doi: 10.1109/JSYST.2018.2890090
3	WANG X H, ZHANG Y, WANG L Z, et al Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory. Chinese Journal of Aeronautics, 2020, 33 (1): 352- 364. doi: 10.1016/j.cja.2019.04.025
4	SUN J B, GE B F, LI J C, et al Operation network modeling with degenerate causal strengths for missile defense systems. IEEE Systems Journal, 2016, 12 (1): 274- 284.
5	QIN M S, ZHAO D L, YANG K W Effectiveness evaluation of anti-submarine activities based on combat network. Systems Engineering and Electronics, 2018, 40 (7): 1513- 1520.
6	LICHTMAN M, VONDAL M T, CLANCY T C, et al Antifragile communications. IEEE Systems Journal, 2016, 12 (1): 659- 670.
7	FANG G X, TAN Y J, ZHANG M, et al Evaluation of relative contribution rate of missile weapon system-of-systems based on combat ring. Systems Engineering and Electronics, 2020, 42 (8): 1734- 1739.
8	CARES J R, DICKMANN J Q. Operations research for unmanned systems. Chichester: Wiley, 2016.
9	THOMAS A, TURNER T, SODERLUND S Net-centric adapter for legacy systems. IEEE Systems Journal, 2009, 3 (3): 336- 342. doi: 10.1109/JSYST.2009.2025813
10	WANG Y M, CHEN S, PAN C S, et al Measure of invulnerability for command and control network based on mission link. Information Sciences, 2018, 426, 148- 159. doi: 10.1016/j.ins.2017.10.035
11	LYV L Y, ZHOU T Link prediction in complex networks: a survey. Physica A: Statistical Mechanics and its Applications, 2011, 390 (6): 1150- 1170. doi: 10.1016/j.physa.2010.11.027
12	LÜ L Y, PAN L M, ZHOU T, et al Toward link predictability of complex networks. Proceedings of the National Academy of Sciences, 2015, 112 (8): 2325- 2330. doi: 10.1073/pnas.1424644112
13	AGHABOZORGI F, KHAYYAMBASHI M R A new similarity measure for link prediction based on local structures in social networks. Physica A: Statistical Mechanics and its Applications, 2018, 501, 12- 23. doi: 10.1016/j.physa.2018.02.010
14	SHERKAT E, RAHGOZAR M, ASADPOUR M Structural link prediction based on ant colony approach in social networks. Physica A:Statistical Mechanics and its Applications, 2015, 419, 80- 94. doi: 10.1016/j.physa.2014.10.011
15	GE B F, LI J C, ZHAO D L, et al Meta-path based link prediction approach for weapon system-of-systems combat networks. Systems Engineering and Electronics, 2019, 41 (5): 1028- 1033.
16	LIBEN-NOWELL D, KLEINBERG J The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 2007, 58 (7): 1019- 1031. doi: 10.1002/asi.20591
17	AL HASAN M, CHAOJI V, SALEM S, et al. Link prediction using supervised learning. Proc. of the Workshop on Link Analysis, Counter-Terrorism and Security, 2006, 30: 798–805.
18	BACKSTROM L, LESKOVEC J. Supervised random walks: predicting and recommending links in social networks. Proc. of the 4th ACM International Conference on Web Search and Data Mining, 2011: 635–644.
19	WANG P, XU B W, WU Y R, et al Link prediction in social networks: the state-of-the-art. Science China: Information Sciences, 2015, 58 (1): 1- 38.
20	GETOOR L, DIEHL C P Link mining: a survey. ACM SIGKDD Explorations Newsletter, 2005, 7 (2): 3- 12. doi: 10.1145/1117454.1117456
21	MENON A K, ELKAN C. Link prediction via matrix factorization. Proc. of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2011: 437-452.
22	VALLES-CATALA T, MASSUCCI F A, GUIMERA R, et al Multilayer stochastic block models reveal the multilayer structure of complex networks. Physical Review X, 2016, 6 (1): 011036. doi: 10.1103/PhysRevX.6.011036
23	ZHANG J. Social network fusion and mining: a survey. arXiv preprint, arXiv: 1804.09874, 2018.
24	DEKKER A Applying social network analysis concepts to military C4ISR architectures. Connections, 2002, 24 (3): 93- 103.
25	DELLER S, RABADI G, TOLK A, et al Organizing for improved effectiveness in networked operations. Military Operations Research, 2012, 17 (1): 5- 16.
26	LEE Y, LEE T Network-based metric for measuring combat effectiveness. Defence Science Journal, 2014, 64 (2): 115- 122. doi: 10.14429/dsj.64.5534
27	FAN C J, LIU Z, LU X, et al An efficient link prediction index for complex military organization. Physica A: Statistical Mechanics and its Applications, 2017, 469, 572- 587. doi: 10.1016/j.physa.2016.11.097
28	SHI C, LI Y T, ZHANG J W, et al A survey of heterogeneous information network analysis. IEEE Trans. on Knowledge and Data Engineering, 2016, 29 (1): 17- 37.
29	QIU J Z, DONG Y X, MA H, et al. Network embedding as matrix factorization: unifying DeepWalk, LINE, PTE, and node2vec. Proc. of the 11th ACM International Conference on Web Search and Data Mining, 2018: 459–467.
30	DONG Y X, CHAWLA N V, SWAMI A. metapath2vec: scalable representation learning for heterogeneous networks. Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017: 135–144.
31	GOYAL P, FERRARA E Graph embedding techniques, applications, and performance: a survey. Knowledge-Based Systems, 2018, 151, 78- 94. doi: 10.1016/j.knosys.2018.03.022
32	PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk: online learning of social representations. Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 701–710.
33	GROVER A, LESKOVEC J. node2vec: scalable feature learning for networks. Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 855–864.
34	TANG J, QU M, WANG M Z, et al. Line: large-scale information network embedding. Proc. of the 24th International Conference on World Wide Web, 2015: 1067-1077.
35	CUI P, WANG X, PEI J, et al A survey on network embedding. IEEE Trans. on Knowledge and Data Engineering, 2018, 31 (5): 833- 852.
36	FU G J, YUAN B, DUAN Q Q, et al. Representation learning for heterogeneous information networks via embedding events. Proc. of the International Conference on Neural Information Processing, 2019: 327–339.
37	GOLDBERG Y, LEVY O. word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv preprint, arXiv: 1402.3722, 2014.
38	LECUN Y, BENGIO Y, HINTON G Deep learning. Nature, 2015, 521 (7553): 436- 444. doi: 10.1038/nature14539
39	AL HASAN M, ZAKI M J. A survey of link prediction in social networks. AGGARWAL C, ed. Social network data analytics. Boston:Springer 2011: 243–275.

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

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 39

相关文章 0

编辑推荐

Metrics

本文评价