Genetic algorithm-based community hiding method in attribute networks

doi:10.11772/j.issn.1001-9081.2024081158

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (9): 2817-2826.DOI: 10.11772/j.issn.1001-9081.2024081158

• Data science and technology • Previous Articles

Genetic algorithm-based community hiding method in attribute networks

Bohan ZHANG¹, Le LYU¹, Junchang JING¹, Dong LIU¹^,²^,³()

^1.School of Computer and Information Engineering，Henan Normal University，Xinxiang Henan 453007，China
^2.Henan Provincial Key Laboratory of Artificial Intelligence and Personalized Learning in Education （Henan Normal University），Xinxiang Henan 453007，China
^3.Big Data Engineering Laboratory of Teaching Resources & Assessment of Education Quality of Henan Province （Henan Normal University），Xinxiang Henan 453007，China

Received:2024-08-16 Revised:2024-12-05 Accepted:2024-12-12 Online:2024-12-17 Published:2025-09-10
Contact: Dong LIU
About author:ZHANG Bohan， born in 1998， M. S. candidate. His research interests include social network analysis.
LYU Le， born in 2000， M. S. candidate. His research interests include social network analysis.
JING Junchang， born in 1990， Ph. D.， lecturer. His research interests include social computing， multimedia content analysis.
Supported by:
National Natural Science Foundation of China(62072160);Henan Provincial Science and Technology Research Program(242102211076)

基于遗传算法的属性网络社区隐藏方法

张博瀚¹, 吕乐¹, 荆军昌¹, 刘栋¹^,²^,³()

^1.河南师范大学计算机与信息工程学院，河南新乡 453007
^2.教育人工智能与个性化学习河南省重点实验室（河南师范大学），河南新乡 453007
^3.教学资源与教育质量评估大数据河南省工程实验室（河南师范大学），河南新乡 453007

通讯作者: 刘栋
作者简介:张博瀚（1998—），男，河南新乡人，硕士研究生，主要研究方向：社会网络分析
吕乐（2000—），男，河南驻马店人，硕士研究生，主要研究方向：社会网络分析
荆军昌（1990—），男，河南焦作人，讲师，博士，CCF会员，主要研究方向：社会计算、多媒体内容分析
基金资助:
国家自然科学基金资助项目(62072160);河南省科技攻关计划项目(242102211076)

Abstract

Abstract:

To counteract community detection algorithms and thereby protect node privacy， community hiding methods have garnered more and more attention. However， current mainstream community hiding algorithms only focus on the network’s topological structure， neglecting the influence of node attributes on community structure， leading to bad performance on attribute networks. In response to these issues， an Attribute network Community hiding method based on Genetic algorithm （ACG） was proposed. In this method， network topological structure and node attributes were integrated， with the core of finding the optimal edge hiding strategy by optimizing a fitness function. In ACG， while minimizing hiding costs， maximizing modularity and attribute similarity was adopted as dual metric to select and perturb the set of edges with the greatest impact on community structure， thereby attacking community detection algorithms for attribute networks effectively. Experimental results demonstrate that without changing the total number of edges and attribute information， the proposed method counters mainstream attribute community detection methods effectively； compared with other community hiding methods， ACG has advantages in counteracting classic community detection algorithms on five attribute networks.

Key words: community hiding, genetic algorithm, attribute network, privacy protection, community detection, social network analysis

摘要：

为了对抗社区检测算法从而实现节点隐私保护，社区隐藏方法得到了越来越多的关注。然而，现有的主流社区隐藏算法仅关注网络的拓扑结构，忽略了节点属性对社区结构的影响，因此在属性网络上表现不佳。针对上述问题，提出一种基于遗传算法的属性网络社区隐藏方法（ACG）。该方法融合网络拓扑结构和节点属性，它的核心在于通过优化适应度函数找到最优的边隐藏策略。ACG在追求最小化隐藏成本的同时，将最大化模块度和属性相似度作为双重度量标准来选择并扰动对社区结构影响最大的边集合，从而实现对属性网络社区检测算法的有效攻击。实验结果表明，在不改变边总数和属性信息的前提下，所提方法有效地对抗了主流的属性社区检测方法；与其他社区隐藏方法相比，ACG在5个属性网络上对抗经典社区检测算法具有优势。

关键词: 社区隐藏, 遗传算法, 属性网络, 隐私保护, 社区检测, 社会网络分析

CLC Number:

TP393

Bohan ZHANG, Le LYU, Junchang JING, Dong LIU. Genetic algorithm-based community hiding method in attribute networks[J]. Journal of Computer Applications, 2025, 45(9): 2817-2826.

张博瀚, 吕乐, 荆军昌, 刘栋. 基于遗传算法的属性网络社区隐藏方法[J]. 《计算机应用》唯一官方网站, 2025, 45(9): 2817-2826.

Figures/Tables 10

Fig. 1 Schematic diagram of attribute network community hiding

Tab. 1 Comparison of ACG with existing community hiding algorithms

社区隐藏算法	数据集	被攻击的算法
DIFFUSER	权重网络	经典网络社区检测算法
ATTSAFDEC	权重网络	经典网络社区检测算法
HC-SBM	拓扑网络	经典网络社区检测算法
LSHA	拓扑网络	经典网络社区检测算法
ACG	属性网络	属性网络社区检测算法

Tab. 2 Symbolic representation

符号	描述
G/G'	原始网络/隐藏后的网络
V/V'	原始节点集合/隐藏后的节点集合
E/E'	原始边集合/隐藏后的边集合
$V A / V A'$	原始属性集合/隐藏后的属性集合
C/C'	原始社区/隐藏后的社区
A_D	社区检测算法
β	修改边数的预算
E⁺	在不同社区之间添加边
E^-	在同一社区内删除边
f	适应度函数
P_c	交叉概率
P_m	突变概率

Tab. 2 Symbolic representation

符号	描述
G/G'	原始网络/隐藏后的网络
V/V'	原始节点集合/隐藏后的节点集合
E/E'	原始边集合/隐藏后的边集合
$V A / V A'$	原始属性集合/隐藏后的属性集合
C/C'	原始社区/隐藏后的社区
A_D	社区检测算法
β	修改边数的预算
E⁺	在不同社区之间添加边
E^-	在同一社区内删除边
f	适应度函数
P_c	交叉概率
P_m	突变概率

Fig. 2 Flow of ACG

Tab. 3 Datasets used in experiments

数据集	节点数	边数	属性数	社区数
Cora	2 708	5 429	1 433	7
WebKB	877	1 608	1 703	5
CiteSeer	3 312	4 732	3 703	6
Terrorists	851	8 592	1 224	4
TerrAtt	1 293	3 743	106	6

Fig. 3 Fitness values corresponding to different parameters Pm and Pc

Tab. 4 Comparison of hiding results between ACG and RAT on Cora dataset

检测算法	度量指标	隐藏算法	β=1%	β=2%	β=3%	β=4%	β=5%	检测算法	度量指标	隐藏算法	β=1%	β=2%	β=3%	β=4%	β=5%
SAC	NMI	RAT	0.862	0.844	0.794	0.805	0.806	ANCA	NMI	RAT	0.793	0.675	0.631	0.573	0.563
	NMI	ACG	0.831	0.827	0.782	0.767	0.738		NMI	ACG	0.712	0.645	0.544	0.580	0.572
	ARI	RAT	0.845	0.801	0.754	0.748	0.726		ARI	RAT	0.788	0.653	0.564	0.480	0.452
	ARI	ACG	0.787	0.750	0.722	0.713	0.694		ARI	ACG	0.589	0.532	0.440	0.462	0.478
	Jaccard	RAT	0.820	0.796	0.747	0.732	0.711		Jaccard	RAT	0.782	0.664	0.569	0.495	0.475
	Jaccard	ACG	0.783	0.745	0.710	0.712	0.687		Jaccard	ACG	0.593	0.537	0.455	0.457	0.471
Inc-Cluster	NMI	RAT	0.894	0.814	0.775	0.716	0.701	CSM	NMI	RAT	0.789	0.757	0.738	0.681	0.659
	NMI	ACG	0.727	0.735	0.670	0.579	0.575		NMI	ACG	0.726	0.701	0.661	0.679	0.648
	ARI	RAT	0.935	0.866	0.826	0.785	0.709		ARI	RAT	0.819	0.762	0.777	0.743	0.720
	ARI	ACG	0.788	0.746	0.671	0.584	0.550		ARI	ACG	0.761	0.718	0.723	0.686	0.672
	Jaccard	RAT	0.794	0.752	0.710	0.659	0.621		Jaccard	RAT	0.700	0.648	0.662	0.631	0.619
	Jaccard	ACG	0.684	0.621	0.565	0.483	0.402		Jaccard	ACG	0.645	0.620	0.625	0.577	0.567

Tab. 5 Hiding results of ACG on four attribute network datasets

度量指标	数据集	SAC			Inc-Cluster			ANCA			CSM
度量指标	数据集	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%
NMI	WebKB	0.805	0.643	0.590	0.729	0.658	0.537	0.697	0.636	0.602	0.767	0.637	0.539
	CiteSeer	0.835	0.738	0.670	0.777	0.674	0.594	0.902	0.824	0.727	0.830	0.714	0.602
	Terrorists	0.870	0.825	0.741	0.782	0.745	0.624	0.764	0.702	0.648	0.891	0.778	0.733
	TerrAtt	0.865	0.842	0.716	0.880	0.752	0.749	0.887	0.718	0.580	0.851	0.627	0.516
ARI	WebKB	0.803	0.636	0.514	0.793	0.718	0.570	0.665	0.570	0.523	0.697	0.459	0.447
	CiteSeer	0.778	0.749	0.614	0.860	0.722	0.567	0.928	0.852	0.748	0.794	0.718	0.653
	Terrorists	0.756	0.725	0.684	0.687	0.617	0.545	0.746	0.675	0.600	0.812	0.767	0.685
	TerrAtt	0.886	0.809	0.629	0.879	0.726	0.712	0.914	0.722	0.489	0.889	0.669	0.551
Jaccard	WebKB	0.789	0.621	0.543	0.701	0.632	0.517	0.654	0.598	0.567	0.723	0.601	0.509
	CiteSeer	0.812	0.704	0.641	0.765	0.669	0.581	0.836	0.801	0.702	0.751	0.692	0.598
	Terrorists	0.845	0.792	0.716	0.754	0.702	0.601	0.732	0.689	0.631	0.867	0.754	0.708
	TerrAtt	0.851	0.823	0.698	0.845	0.731	0.724	0.762	0.691	0.567	0.721	0.609	0.531

Tab. 6 Comparison of NMI results of ACG and different community hiding algorithms

数据集	隐藏算法	Louvain			Walktrap			LPA			Greedy
数据集	隐藏算法	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%
WebKB	ACG	0.892	0.800	0.726	0.821	0.805	0.754	0.831	0.753	0.715	0.907	0.825	0.739
	DIFFUSER	0.914	0.889	0.853	0.905	0.851	0.817	0.937	0.893	0.853	0.934	0.901	0.857
	ATTSAFDEC	0.942	0.906	0.826	0.897	0.860	0.791	0.919	0.873	0.834	0.918	0.879	0.855
	HC-SBM	0.868	0.854	0.789	0.876	0.832	0.770	0.893	0.801	0.728	0.909	0.807	0.725
	LSHA	0.894	0.819	0.759	0.963	0.893	0.796	0.835	0.779	0.749	0.972	0.847	0.817
Cora	ACG	0.828	0.744	0.630	0.831	0.797	0.746	0.849	0.836	0.829	0.855	0.786	0.724
	DIFFUSER	0.940	0.868	0.774	0.918	0.861	0.786	0.948	0.902	0.797	0.943	0.894	0.832
	ATTSAFDEC	0.933	0.875	0.780	0.905	0.863	0.817	0.927	0.891	0.856	0.915	0.847	0.795
	HC-SBM	0.854	0.763	0.655	0.876	0.782	0.729	0.864	0.847	0.771	0.878	0.801	0.748
	LSHA	0.832	0.803	0.728	0.971	0.847	0.794	0.866	0.838	0.808	0.903	0.813	0.712
CiteSeer	ACG	0.908	0.851	0.784	0.915	0.859	0.811	0.923	0.884	0.852	0.908	0.849	0.784
	DIFFUSER	0.920	0.836	0.765	0.931	0.874	0.823	0.944	0.869	0.824	0.921	0.838	0.776
	ATTSAFDEC	0.934	0.853	0.770	0.957	0.916	0.884	0.951	0.902	0.850	0.926	0.877	0.803
	HC-SBM	0.921	0.876	0.802	0.928	0.894	0.837	0.946	0.901	0.869	0.924	0.865	0.796
	LSHA	0.937	0.912	0.893	0.989	0.968	0.906	0.938	0.922	0.911	0.956	0.914	0.891
Terrorists	ACG	0.927	0.902	0.871	0.859	0.784	0.697	0.936	0.911	0.892	0.890	0.853	0.842
	DIFFUSER	0.963	0.924	0.880	0.965	0.932	0.862	0.958	0.926	0.870	0.952	0.879	0.821
	ATTSAFDEC	0.961	0.927	0.884	0.952	0.918	0.879	0.960	0.922	0.875	0.924	0.874	0.816
	HC-SBM	0.942	0.915	0.866	0.878	0.801	0.715	0.930	0.914	0.841	0.903	0.857	0.825
	LSHA	0.947	0.878	0.835	0.963	0.905	0.854	0.889	0.841	0.802	0.945	0.814	0.761
TerrAtt	ACG	0.976	0.932	0.917	0.843	0.794	0.643	0.903	0.849	0.826	0.930	0.867	0.842
	DIFFUSER	0.898	0.830	0.799	0.921	0.881	0.826	0.925	0.876	0.841	0.918	0.879	0.855
	ATTSAFDEC	0.904	0.864	0.813	0.952	0.890	0.819	0.943	0.887	0.850	0.950	0.891	0.827
	HC-SBM	0.928	0.845	0.804	0.865	0.812	0.760	0.915	0.868	0.840	0.928	0.882	0.856
	LSHA	0.965	0.912	0.863	0.966	0.913	0.893	0.963	0.865	0.828	0.964	0.847	0.805

Tab. 7 Comparison of ARI results of ACG and different community hiding algorithms

数据集	隐藏算法	Louvain			Walktrap			LPA			Greedy
数据集	隐藏算法	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%	β=1%	β=3%	β=5%
WebKB	ACG	0.773	0.688	0.661	0.738	0.694	0.643	0.760	0.667	0.658	0.802	0.740	0.713
	DIFFUSER	0.803	0.701	0.676	0.837	0.765	0.680	0.826	0.789	0.671	0.819	0.816	0.767
	ATTSAFDEC	0.824	0.759	0.684	0.780	0.731	0.641	0.818	0.752	0.656	0.790	0.748	0.702
	HC-SBM	0.792	0.705	0.678	0.765	0.712	0.655	0.784	0.689	0.674	0.820	0.762	0.730
	LSHA	0.828	0.723	0.671	0.922	0.818	0.619	0.712	0.625	0.598	0.966	0.794	0.759
Cora	ACG	0.760	0.714	0.678	0.748	0.702	0.696	0.766	0.734	0.680	0.765	0.733	0.674
	DIFFUSER	0.832	0.775	0.714	0.808	0.766	0.694	0.837	0.788	0.727	0.813	0.779	0.742
	ATTSAFDEC	0.821	0.754	0.685	0.804	0.741	0.710	0.801	0.784	0.720	0.799	0.727	0.684
	HC-SBM	0.766	0.721	0.654	0.864	0.748	0.651	0.783	0.721	0.652	0.810	0.762	0.598
	LSHA	0.796	0.740	0.635	0.945	0.647	0.589	0.776	0.701	0.620	0.848	0.739	0.582
CiteSeer	ACG	0.743	0.688	0.670	0.784	0.752	0.719	0.793	0.763	0.716	0.782	0.764	0.720
	DIFFUSER	0.781	0.731	0.654	0.821	0.784	0.739	0.808	0.731	0.671	0.827	0.760	0.711
	ATTSAFDEC	0.745	0.717	0.657	0.840	0.776	0.763	0.795	0.755	0.704	0.796	0.753	0.699
	HC-SBM	0.804	0.715	0.641	0.903	0.821	0.766	0.745	0.674	0.632	0.847	0.765	0.628
	LSHA	0.762	0.695	0.623	0.946	0.851	0.758	0.714	0.658	0.586	0.792	0.683	0.564
Terrorists	ACG	0.816	0.764	0.741	0.793	0.721	0.697	0.831	0.791	0.732	0.788	0.737	0.728
	DIFFUSER	0.833	0.782	0.735	0.814	0.809	0.758	0.861	0.806	0.730	0.801	0.730	0.659
	ATTSAFDEC	0.813	0.775	0.724	0.846	0.795	0.789	0.874	0.754	0.710	0.794	0.722	0.674
	HC-SBM	0.845	0.788	0.715	0.847	0.794	0.727	0.784	0.725	0.683	0.847	0.762	0.619
	LSHA	0.933	0.854	0.726	0.948	0.892	0.774	0.769	0.712	0.688	0.923	0.746	0.658
TerrAtt	ACG	0.810	0.786	0.762	0.769	0.718	0.630	0.789	0.734	0.703	0.798	0.768	0.716
	DIFFUSER	0.779	0.704	0.691	0.817	0.760	0.717	0.784	0.720	0.661	0.781	0.745	0.698
	ATTSAFDEC	0.788	0.734	0.703	0.804	0.770	0.729	0.813	0.723	0.719	0.820	0.781	0.712
	HC-SBM	0.763	0.681	0.614	0.842	0.785	0.662	0.791	0.701	0.656	0.754	0.702	0.648
	LSHA	0.766	0.653	0.582	0.778	0.722	0.649	0.806	0.693	0.613	0.762	0.729	0.645

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Genetic algorithm-based community hiding method in attribute networks

基于遗传算法的属性网络社区隐藏方法

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