基于改进加权LeaderRank的目标人员重要度排序算法

doi:10.3969/j.issn.1671-1122.2024.11.006

摘要/Abstract

摘要：

针对当前人工分析复杂人际关系数据时面临的挑战，尤其是对重要个体关联人员重要性评估时存在准确率不足、效率低及成本高等问题，文章综合考量该类人员行为特征和活动规律，基于重点人员的话单数据和加权LeaderRank算法，对通话时长、通话次数、夜间通话频次和联系人中重点人员数量等多因素进行权重分配，提出一种改进的加权LeaderRank算法，并对重点人员的通联关系人重要程度进行排序，筛选出与重要个体具有类似行为模式和活动特性的目标人员。实验结果表明，改进加权LeaderRank算法与经典的影响力节点发现算法如节点度中心性算法、接近中心性算法和介数中心性算法相比，对于通联关系中具有类似行为特征的目标人员的分值更高，能够有效识别通联关系中潜在的、不易察觉的目标人员。

关键词: LeaderRank, 复杂网络分析, 节点重要度排序, 关联挖掘

Abstract:

At present, the manual analysis of complex interpersonal relationship data is faced with challenges, especially the problems of insufficient accuracy, low efficiency and high cost for the importance assessment of important individuals. To solve this problem, this paper comprehensively considered behavioral characteristics and activity rules of this type of personnel, based on call detail records of key personnel and the weighted LeaderRank algorithm, and assigned weight to multiple factors such as call duration, call frequency, night call frequency and the number of key individuals among contacts. As a result, it proposed an improved weighted LeaderRank algorithm to rank the importance of key contacts and screen out target people with similar behavior patterns and activity characteristics as important individuals. The experimental results show that compared with classical influence node discovery algorithms such as the degree centrality algorithm, the closeness centrality algorithm and the betweenness centrality algorithm, the improved weighted LeaderRank algorithm has a higher score for target people with similar behavior characteristics in the communication relationship, and can effectively identify potential and unobserved target people in the communication relationship.

Key words: LeaderRank, complex network analysis, node importance ranking, association mining

中图分类号:

TP309

夏玲玲, 马卓, 郭向民, 倪雪莉. 基于改进加权LeaderRank的目标人员重要度排序算法[J]. 信息网络安全, 2024, 24(11): 1665-1674.

XIA Lingling, MA Zhuo, GUO Xiangmin, NI Xueli. Target Personnel Importance Ranking Algorithm Based on Improved Weighted LeaderRank[J]. Netinfo Security, 2024, 24(11): 1665-1674.

图/表 4

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话单属性	话单属性（英文）	属性值（*号标识0~9数字）
主被叫标志（呼叫方向）	Direction	主叫/被叫
事件发生地（电话区号）	Location	南京市
本方号码（手机号）	S_tel	130********
IMEI码（手机串号）	IMEI	8671**********
本方号码归属地	S_attribution	**市
位置区标识码	PIC	133**
小区标识码	CI	213**
基站经度	Longitude	117.77***
基站纬度	Latitude	34.*****
通话开始时间	Stime	2018-12-25 13:40:27
通话时长（s）	Seconds	12
对方号码（手机号）	T_tel	138********
对方号码归属地	T_attribution	南京市
基站名称	Basestation	NJ_GL_HW_***村电信_G_C_G

节点编号（Tel）	3896	3932	4026
是否重点人员（Suspect（yes/no））	no	yes	yes
联系人数量（Contact_num）	26人	460人	116人
重点人员数量（Sus_Contact_num）	26人	7人	4人
IMEI数量（IMEI_num）	0个	2个	7个
基站名称（Basestation_First）	0	大庆***	晏公***

主被叫（Direction）	100	200	200
本方（S_tel）	13914	13601	13556
对方（T_tel）	9512	8943	13809
IMEI数量（IMEIS）	1个	1个	2个
夜间通话次数（Night_times）	2次	4次	8次
总通话次数（Call_count）	138次	4次	22次
通话总时长（Sum_calltime）	6735 s	305 s	1333 s
最大通话时长（Max_calltime）	579 s	102 s	437 s
最小通话时长（Min_calltime）	4 s	24 s	1 s

排名	手机号	改进的加权LeaderRank算法分值	度中心性算法分值	接近中心性算法分值	介数中心性算法分值
1	5873	0.99399817	0.24	0.61199456	0.062003609
2	9536	0.95835223	0.08	0.60517637	0.173175853
3	13033	0.93085113	0.12	0.55886427	0.101986866
4	4756	0.91614471	0.16	0.5653326	0.040812103
5	8160	0.85990686	0.12	0.57709771	0.052809148
6	1661	0.83528273	0.08	0.56015409	0.035128633
7	7538	0.81830179	0.12	0.51734709	0.020954698
8	11111	0.80784825	0.08	0.53974379	0.038104458
9	8830	0.79455319	0.12	0.48719124	0.037361958
10	13854	0.77905092	0.08	0.52598724	0.023063675
11	3032	0.74990686	0.12	0.44447684	0.057778143
12	2783	0.67528273	0.08	0.51489453	0.021622982
13	4612	0.65830179	0.12	0.47076744	0.024140619
14	5038	0.65784825	0.12	0.42557669	0.063028967
15	8994	0.64455319	0.12	0.4321969	0.055034893
16	4050	0.62905092	0.08	0.47425952	0.022367555
17	4328	0.62225499	0.04	0.47776641	0.058674394
18	4245	0.61651752	0.04	0.51734709	0.014568814
19	3933	0.61490806	0.08	0.46729007	0.011531529
20	7531	0.60860566	0.04	0.50394516	0.014492739