Dynamic Multi-objective Optimization for Rumor Control in Time-varying Social Networks

LI Jiting; SUN Yi; GUO Hao; SONG Yanjie; OU Junwei; JIA Jun

doi:10.11999/JEIT260646

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2026 >

LI Jiting, SUN Yi, GUO Hao, SONG Yanjie, OU Junwei, JIA Jun. Dynamic Multi-objective Optimization for Rumor Control in Time-varying Social Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260646

Citation:

LI Jiting, SUN Yi, GUO Hao, SONG Yanjie, OU Junwei, JIA Jun. Dynamic Multi-objective Optimization for Rumor Control in Time-varying Social Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260646

Citation:

PDF( 2447 KB)

Dynamic Multi-objective Optimization for Rumor Control in Time-varying Social Networks

doi: 10.11999/JEIT260646 cstr: 32379.14.JEIT260646

LI Jiting¹,
SUN Yi^{1
,
,},
GUO Hao¹,
SONG Yanjie²,
OU Junwei³,
JIA Jun¹

1.
Academy of Military Sciences, Beijing 100097, China
2.
National Defense University, Beijing 100091, China
3.
School of Computer Science, Xiangtan University, Xiangtan, Hunan 411105, China

Received Date: 2026-05-15
Accepted Date: 2026-08-13
Rev Recd Date: 2026-06-23

Available Online: 2026-09-17

Abstract

Abstract

Objective The rapid dissemination of rumors in online social networks (OSNs) poses significant threats to societal stability. Counter-rumor strategy, which combats misinformation by proactively spreading factual information, is a promising non-intrusive countermeasure. Its essence is a resource-constrained optimization problem: selecting an optimal set of seed users to initiate the counter-rumor, aiming to simultaneously minimize the rumor's final influence and the intervention cost. However, existing research predominantly relies on a static network assumption, neglecting the intrinsic dynamic nature of OSNs where user influence, activity, and susceptibility evolve over time and are perturbed by external events. Strategies optimized for a static snapshot of the network often fail when the environment changes, highlighting a critical gap. This work addresses this gap by formally modeling the problem as a Dynamic Multi-objective Optimization Problem (DMOP). The necessity lies in developing an adaptive framework that can track the time-varying optimal trade-off between control effectiveness and resource expenditure, which is essential for robust and practical rumor governance systems. Methods First, a Dynamic Belief-driven Competitive Cascade (DyBCC) model is proposed. Its core innovation is the time-varying user belief, B(v,t), which synthesizes static structural influence with a dynamic component driven by natural decay and external event shocks (Definition 2.1), providing a realistic stochastic simulation framework for competitive propagation under dynamic environments. Second, based on DyBCC, the counter-rumor seed selection is formalized as a Dynamic Multi-objective Optimization Problem for Rumor Control (DMOP-RC), with objectives to minimize expected rumor impact f₁ and seed cost f₂, where both the objective function and the Pareto-optimal set change over time. Third, to solve DMOP-RC, a Prediction-Memory guided Multi-population Evolutionary Algorithm (PM3EA) is designed. It maintains three collaborative populations: a main population P_m using MOEA/D for local exploitation, a memory archive P_a storing historical elites, and an exploration population P_e using Differential Evolution for global search. A key mechanism is its adaptive response to detected environmental changes (Algorithm 1). Results and Discussions The proposed framework is evaluated in a dynamic environment constructed from a real-world OSN dataset. Algorithm Performance: PM3EA is compared against three dynamic multi-objective optimizers. In terms of the comprehensive Dynamic Inverted Generational Distance (DIGD), PM3EA achieves a significantly superior value of 0.0996±0.0569 (Table 2, Fig. 3b). Visually, the final Pareto front obtained by PM3EA is the closest to the reference front and shows the best distribution (Fig. 3a). Its DIGD value remains consistently the lowest and most stable across all time windows, demonstrating robust tracking capability (Fig. 3c). Model and Strategy Validation: A detailed case study provides micro- and macro-level insights. The evolution of individual node beliefs visually demonstrates the dynamic competition captured by the DyBCC model, showing patterns like belief oscillation in rumor seeds and sudden "clearance" in some nodes upon effective counter-rumor exposure (Fig. 5). At the macro level, the effectiveness of the rumor control method is demonstrated on three datasets of different scales (Fig. 6), quantitatively proving the suppression effect of the derived seed set. Conclusions This work systematically tackles adaptive rumor control in dynamic social networks. The primary contributions are: 1) The DyBCC model effectively captures the core dynamic of information competition by integrating time-varying user belief, providing a more realistic foundation than static models. 2) The DMOP-RC formulation correctly frames the seed selection as a dynamic trade-off, aligning with practical needs. 3) The PM3EA algorithm, with its tri-population synergy and adaptive change-response mechanisms, demonstrates superior performance in tracking the time-varying Pareto front, outperforming established counterparts. The experiments, from algorithm comparison to case study, holistically validate the effectiveness and superiority of the proposed framework. This work provides a complete methodology for developing intelligent, self-adaptive online rumor governance systems. Future work may explore integrating more complex user behavior models and online learning mechanisms.
- Time-varying social network,
- Rumor control,
- Dynamic multi-objective optimization,
- Competitive cascade model,
- Evolutionary algorithm

FullText(HTML)

References(22)

References

[1]	WU Shufang, GAO Mengjiao, and ZHU Jie. Considering dual-followers to dynamically model and analyze online rumor propagation[J]. Information Processing & Management, 2026, 63(1): 104301. doi: 10.1016/j.ipm.2025.104301.
[2]	胡泽, 陈志南, 杨宏宇. 多源特征融合增强的虚假新闻检测方法[J]. 电子与信息学报, 2025, 47(8): 2919–2934. doi: 10.11999/JEIT250041. HU Ze, CHEN Zhinan, and YANG Hongyu. A fake news detection approach enhanced by multi-source feature fusion[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2919–2934. doi: 10.11999/JEIT250041.
[3]	王艳, 孙钦东, 荣东柱, 等. 伪影间共性机理驱动的多域感知社交网络深度伪造视频检测[J]. 电子与信息学报, 2024, 46(9): 3713–3721. doi: 10.11999/JEIT240025. WANG Yan, SUN Qindong, RONG Dongzhu, et al. Deepfake video detection on social networks using multi-domain aware driven by common mechanism analysis between artifacts[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3713–3721. doi: 10.11999/JEIT240025.
[4]	YU Zhenhua, YAN Rumeng, WU Shixing, et al. Dynamic analysis and immune control strategy of a rumor propagation model considering network topology[J]. Chaos, Solitons & Fractals, 2026, 208: 118287. doi: 10.1016/j.chaos.2026.118287.
[5]	HUANG Dawen, WU Wenjie, BI Jichao, et al. Timeliness-aware rumor sources identification in community-structured dynamic online social networks[J]. Information Sciences, 2025, 689: 121508. doi: 10.1016/j.ins.2024.121508.
[6]	XIA Yang, JIANG Haijun, and YU Shuzhen. Exploring the dynamics of group-based internet rumors propagation: A novel model from the perspective of random hypergraphs[J]. Information Processing & Management, 2025, 62(1): 103941. doi: 10.1016/j.ipm.2024.103941.
[7]	DONG Chen, XU Guiqiong, and MENG Lei. CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization[J]. Chinese Physics B, 2024, 33(8): 088901. doi: 10.1088/1674-1056/ad531f.
[8]	ZIARI M and MOGHADDAM CHARKARI N. Rumor detection and propagation on social networks: A survey[J]. Expert Systems with Applications, 2026, 295: 128798. doi: 10.1016/j.eswa.2025.128798.
[9]	DING Xuejun, LI Mengyu, TIAN Yong, et al. RBOTUE: Rumor blocking considering outbreak threshold and user experience[J]. IEEE Transactions on Engineering Management, 2024, 71: 13396–13414. doi: 10.1109/TEM.2021.3111640.
[10]	荆军昌, 张志勇, 班爱莹, 等. 关键节点双目标优化的虚假信息传播控制模型[J]. 西安电子科技大学学报, 2024, 51(1): 201–209. doi: 10.19665/j.issn1001-2400.20230209. JING Junchang, ZHANG Zhiyong, BAN Aiying, et al. Disinformation spreading control model based on key nodes bi-objective optimization[J]. Journal of Xidian University, 2024, 51(1): 201–209. doi: 10.19665/j.issn1001-2400.20230209.
[11]	PARIMI P and ROUT R R. Genetic algorithm based rumor mitigation in online social networks through counter-rumors: A multi-objective optimization[J]. Information Processing & Management, 2021, 58(5): 102669. doi: 10.1016/j.ipm.2021.102669.
[12]	TONG Guangmo, WU Weili, GUO Ling, et al. An efficient randomized algorithm for rumor blocking in online social networks[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(2): 845–854. doi: 10.1109/TNSE.2017.2783190.
[13]	TANG Zhen, HE Qiang, JIANG Runze, et al. Stop rumors fast: A multiobjective blocking approach with deep reinforcement learning in social networks[J]. IEEE Transactions on Artificial Intelligence, 2026, 7(3): 1430–1442. doi: 10.1109/TAI.2025.3597883.
[14]	KEMPE D, KLEINBERG J, and TARDOS É. Maximizing the spread of influence through a social network[C]. The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, USA, 2003: 137–146. doi: 10.1145/956750.956769.
[15]	BUDAK C, AGRAWAL D, and EL ABBADI A. Limiting the spread of misinformation in social networks[C]. The 20th International Conference on World Wide Web, Hyderabad, India, 2011: 665–674. doi: 10.1145/1963405.1963499.
[16]	EBBINGHAUS H, RUGER H A, and BUSSENIUS C E, translation. Memory: A Contribution to Experimental Psychology[M]. Teachers College Press, 1913. doi: 10.1037/10011-000. (查阅网上资料,未找到对应的出版地和页码信息,请确认)(查阅网上资料,doi打不开,请确认).
[17]	MYERS S A, ZHU Chenguang, and LESKOVEC J. Information diffusion and external influence in networks[C]. The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, 2012: 33–41. doi: 10.1145/2339530.2339540.
[18]	ZHANG Qingfu and LI Hui. MOEA/D: A multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712–731. doi: 10.1109/TEVC.2007.892759.
[19]	OPSAHL T and PANZARASA P. Clustering in weighted networks[J]. Social Networks, 2009, 31(2): 155–163. doi: 10.1016/j.socnet.2009.02.002.
[20]	CAO Leilei, XU Lihong, GOODMAN E D, et al. Evolutionary dynamic multiobjective optimization assisted by a support vector regression predictor[J]. IEEE Transactions on Evolutionary Computation, 2020, 24(2): 305–319. doi: 10.1109/TEVC.2019.2925722.
[21]	JIANG Min, HUANG Zhongqiang, QIU Liming, et al. Transfer learning-based dynamic multiobjective optimization algorithms[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(4): 501–514. doi: 10.1109/TEVC.2017.2771451.
[22]	PENG Zhou, ZHENG Jinhua, ZOU Juan, et al. Novel prediction and memory strategies for dynamic multiobjective optimization[J]. Soft Computing, 2015, 19(9): 2633–2653. doi: 10.1007/s00500-014-1433-3.