Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (6): 49-58.doi: 10.6040/j.issn.1672-3961.0.2021.281

• Machine Learning & Data Mining • Previous Articles    

Cross social network user alignment via fusing node state information

Jun HU1,2(),Dongmei YANG1,2,Li LIU1,2,Fujin ZHONG1,2   

  1. 1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    2. Chongqing Key Laboratory of Computing Intelligence(Chongqing University of Posts and Telecommunications), Chongqing 400065, China
  • Received:2021-05-28 Online:2021-12-20 Published:2022-01-19

Abstract:

A cross social network user alignment method by fusing node state information was proposed. The local characteristics of nodes and node state information were captured through network representation to obtain the embedded vector of each account, and the aligned users were found by calculating the similarity between corresponding representations of different accounts. Experimental results on two real data sets showed that the proposed method could align more users than other methods. When predicting top-k of different scales, the proposed method could achieve an alignment precision of 50% at top-9 on the data set Twitter-Foursquare with dense network structure. Compared with other methods on the sparse and large network data set DM-ML, the improvement on alignment precision was 12.06%-36.62%. The analysis of F1-score also showed that the proposed method could effectively improve the performance of user alignment.

Key words: user alignment, social network, local characteristics, node states, network embedding

CLC Number: 

  • TP391

Fig.1

The network alignment method flow via fusing node state information"

Table 1

The datasets"

数据集 数据子集 节点数 边数 锚节点数
DM-ML DBLP-Data mining 11 526 47 326 1295
DBLP-Machine learning 12 311 43 948
Twitter-Foursquare Twitter 5120 164 919 1609
Foursquare 5313 76 972

Table 2

The alignment precision under different k on Twitter-Foursquare"

k UAFNS IONE DeepLink Mego2Vec PALE CrossMNA
1 0.274 7 0.205 6 0.159 4 0.145 6 0.101 2 0.015 5
5 0.435 1 0.357 5 0.312 6 0.227 8 0.208 8 0.093 2
9 0.501 6 0.439 5 0.449 3 0.291 1 0.272 1 0.183 2
17 0.564 9 0.541 1 0.551 3 0.411 4 0.360 7 0.257 7
21 0.590 4 0.566 4 0.566 8 0.436 7 0.386 0 0.291 9
25 0.604 1 0.572 7 0.584 8 0.468 4 0.411 3 0.338 5
30 0.624 0 0.601 2 0.597 3 0.481 0 0.436 7 0.363 3

Table 3

The alignment precision under different k on DM-ML"

k UAFNS IONE DeepLink Mego2Vec PALE CrossMNA
1 0.190 0 0.146 1 0.183 8 0 0.044 1 0.019 2
5 0.377 6 0.220 8 0.367 6 0.038 0 0.279 4 0.223 0
9 0.452 5 0.283 4 0.422 8 0.050 6 0.338 2 0.307 6
17 0.525 1 0.354 4 0.477 9 0.132 9 0.375 0 0.392 3
21 0.543 6 0.378 4 0.489 0 0.202 5 0.382 4 0.423 0
25 0.559 1 0.398 5 0.503 7 0.240 5 0.382 4 0.434 6
30 0.581 5 0.420 8 0.511 0 0.253 2 0.389 7 0.450 0

Fig.2

The alignment precision under different training ratios on Twitter-Foursquare"

Fig.3

The alignment precision under different training ratios on DM-ML"

Fig.4

F1 under different training ratios"

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