银行交易网络的链路预测

doi:10.16180/j.cnki.issn1007-7820.2019.05.011

Abstract

Abstract:

Based on the dynamic changes in bank transactions and the characteristics of timeliness and repeatability, the basic topology statistical properties and clustering structure of the bank's network were studied, and obtained the transaction network satisfied with the small-world and scale-free characteristics.Based on the deficiency of existing link prediction algorithms in dynamic network prediction, a new dynamic link algorithm was proposed to predict bank customer transactions. Then, based on the algorithm mentioned above, two characteristics, the three predictive algorithms combined with the random algorithm were compared. These three algorithms were applied to the three types of real data sets with dynamic transaction characteristics for experimental verification. The results showed that the prediction accuracy of the algorithm was about 75%. Finally, comparing the algorithm with the classical prediction algorithm, the proposed algorithm improved the prediction by 5% to 10%.

Key words: complex network, link prediction, bank network, node centrality, strength and weakness, accuracy

CLC Number:

TP391

MA Qingqing,YAN Guanghui,WANG Yafei,WU Yu. Link Prediction on Bank Transaction Network[J].Electronic Science and Technology, 2019, 32(5): 55-62.

Figures/Tables 10

Figure 1.

Table 1

Local information based similarity indicator"

预测指标	公式
CN指标^[9]	S_xy=\|Γ(x)∩Γ(y)\|
Salton相似性指标^[10]	S_xy= $Γ (x) ? Γ (y) \| k x k y$
Jaccard相似性指标	S_xy= $Γ (x) ? Γ (y) \| Γ (x) ? Γ (y) \|$
S?renson相似性指标^[11]	S_xy= $2 × Γ (x) ? Γ (y) \| k x + k y$
大度节点有利指标^[12]	S_xy= $Γ (x) ? Γ (y) \| min {k x k y}$
大度节点不利指标(HDI)	S_xy= $Γ (x) ? Γ (y) \| max {k x k y}$
LHN-1^[13]	S_xy= $Γ (x) ? Γ (y) \| k x k y$
AA指标^[14]	$S xy = ∑ z ∈ Γ (x) ? Γ (y) 1 log k z$
RA指标	$S xy = ∑ z ∈ Γ (x) ? Γ (y) 1 k z$
PA指标^[15]	S_xy=k_xk_y

Table 1

Figure 2.

Figure 3.

Table 2

Figure 4.

Figure 5.

Table 3

Figure 6.

Figure 7.

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时间段	节点个数	网络平均聚类系数
1月份	1 277 653	0.042 2
2月份	1 159 472	0.045 6
3月份	1 266 388	0.042 8

	节点数目	边数	时间跨度
Wiki-talk	1 140 149	7 833 140	2 320天
Email- Eu -Core	986	332 334	803天
Math-Overflow	24 818	506 550	2 350天

Link Prediction on Bank Transaction Network

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