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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (9): 88-99    DOI: 10.11925/infotech.2096-3467.2018.0342
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Classifying Multilayer Social Network Links Based on Transfer Component Analysis
Wu Jiehua1,2(), Shen Jing1, Zhou Bei1
1College of Computer Science and Information Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China
2School of Computer and Engineering, South China University of Technology, Guangzhou 510641, China
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Abstract  

[Objective] The paper aims to address the issues facing multi-layer social network link classification algorithms, which cannot effectively correlate information among sub-networks to improve classification. [Methods] First, we defined the common features reflecting the correlation between sub-network. Then we defined individuality features reflecting the characteristics of each sub-network’s own attributes. Third, we proposed an algorithm to classify multilayer social network links based on transfer component analysis. This algorithm collects characteristics of the correlation between layers, which makes sub-networks learn from each other. [Results] We compared the proposed model with the benchmark classification algorithm, feature selection based classification algorithm, and the benchmark transfer based classification algorithm on two real multi-layer datasets from YouTube and QueryLog. The performance of our algorithm on evaluation metrics of AUC and ROC curves were significantly improved. The evaluation index of the larger promotion curve has at least 1.57% and at most 33.2% improvement. [Limitations] We did not examine very large-scale network data with the proposed model. The relationship between the layers and performance of feature definition needs more discussion. [Conclusions] The proposed method effectively applies transfer learning to the classification of multilayer social network links and offers new directions for future studies.
Key words： Multilayer Network      Social Network      Link Classification      Component Analysis      Transfer Learning     
Received: 28 March 2018      Published: 25 October 2018
ZTFLH:  分类号: TP391 G35  
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Jiehua Wu
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Cite this article:

Wu Jiehua,Shen Jing,Zhou Bei. Classifying Multilayer Social Network Links Based on Transfer Component Analysis. Data Analysis and Knowledge Discovery, 2018, 2(9): 88-99.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0342     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I9/88

网络 节点 关系 链接 平均度 聚类系数 平均共邻节点
YouTube 1 000 Friends(F) 8 915 36.61 0.56 6.14
Subscriptions(S) 31 282 129.59 0.71 21.74
Videos(V) 11 472 47.44 0.52 9.34
QueryLog 757 Bin1 13 764 36.36 0.53 10.18
Bin2 11 994 31.69 0.49 8.49
Bin3 9 037 22.29 0.44 6.77
r=0.8 S→F V→F F→S V→S F→V S→V
LR 0.5168(±0.267) 0.5173(±0.225) 0.5188(±0.326) 0.4872(±0.207) 0.5528(±0.317) 0.5498(±0.382)
SVM 0.5771(±0.571) 0.5747(±0.548) 0.5742(±0.595) 0.5786(±0.601) 0.5949(±0.573) 0.5977(±0.662)
FS-LR 0.5304(±0.389) 0.5337(±0.527) 0.5279(±0.636) 0.5263(±0.676) 0.6091(±0.882) 0.6042(±0.832)
FS-SVM 0.6042(±0.428) 0.6066(±0.444) 0.5848(±0.532) 0.5853(±0.593) 0.6345(±0.712) 0.6336(±0.688)
TLR 0.5207(±0.396) 0.6587(±0.356) 0.5682(±0.428) 0.5526(±0.433) 0.6257(±0.498) 0.5531(±0.524)
TSVM 0.6323(±0.436) 0.6911(±0.424) 0.6359(±0.449) 0.6343(±0.382) 0.6967(±0.507) 0.5699(±0.442)
TCA 0.6889(±0.547) 0.7048(±0.627) 0.7789(±0.673) 0.7548(±0.752) 0.8109(±0.736) 0.7925(±0.627)
r =0.9 S→F V→F F→S V→S F→V S→V
LR 0.4936(±0.243) 0.4939(±0.182) 0.4632(±0.221) 0.4628(±0.234) 0.5364(±0.322) 0.5345(±0.442)
SVM 0.5592(±0.615) 0.5603(±0.627) 0.5586(±0.532) 0.5577(±0.657) 0.5862(±0.581) 0.5862(±0.626)
FS-LR 0.5112(±0.434) 0.5118(±0.425) 0.5029(±0.453) 0.5049(±0.417) 0.5703(±0.633) 0.5853(±0.548)
FS-SVM 0.5855(±0.508) 0.5873(±0.486) 0.5676(±0.597) 0.5774(±0.536) 0.6035(0.727) 0.6044(±0.793)
TLR 0.4922(±0.354) 0.6411(±0.415) 0.5421(±0.428) 0.5281(±0.519) 0.6011(±0.463) 0.5352(±0.457)
TSVM 0.6068(±0.487) 0.6748(±0.397) 0.6077(±0.458) 0.6081(±0.425) 0.6752(±0.686) 0.5524(±0.417)
TCA 0.6677(±0.679) 0.6854(±0.682) 0.7638(±0.774) 0.7328(±0.746) 0.7924(±0.611) 0.7781(±0.615)
r=0.8 Bin2→Bin1 Bin3→Bin1 Bin1→Bin2 Bin3→Bin2 Bin1→Bin3 Bin2→Bin3
LR 0.5425(±0.284) 0.5476(±0.245) 0.4983(±0.309) 0.5184(±0.228) 0.4947(±0.302) 0.5058(±0.359)
SVM 0.5492(±0.456) 0.5487(±0.472) 0.5624(±0.544) 0.5779(±0.430) 0.5237(±0.573) 0.5277(±0.662)
FS-LR 0.5480(±0.339) 0.5471(±0.315) 0.4957(±0.582) 0.5187(±0.628) 0.4999(±0.857) 0.5132(±0.741)
FS-SVM 0.5523(±0.229) 0.5538(±0.254) 0.5672(±0.381) 0.5798(±0.430) 0.6345(±0.554) 0.6336(±0.513)
TLR 0.5472(±0.297) 0.5477(±0.331) 0.5080(±0.366) 0.5194(±0.327) 0.5008(±0.439) 0.5079(±0.515)
TSVM 0.5508(±0.302) 0.5532(±0.326) 0.5312(±0.385) 0.5866(±0.374) 0.5335(±0.487) 0.5332(±0.468)
TCA 0.8108(±1.782) 0.8176(±1.856) 0.7782(±1.654) 0.7582(±1.788) 0.8024(±1.459) 0.8045(±1.565)
r =0.9 Bin2→Bin1 Bin3→Bin1 Bin1→Bin2 Bin3→Bin2 Bin1→Bin3 Bin2→Bin3
LR 0.5286(±0.298) 0.5278(±0.122) 0.4747(±0.215) 0.4802(±0.272) 0.4957(±0.303) 0.4981(±0.371)
SVM 0.5428(±0.475) 0.5433(±0.472) 0.5482(±0.528) 0.5492(±0.552) 0.5039(±0.466) 0.5082(±0.549)
FS-LR 0.5288(±0.418) 0.5282(±0.413) 0.4832(±0.339) 0.4884(±0.306) 0.4998(±0.462) 0.5074(±0.505)
FS-SVM 0.5467(±0.505) 0.5472(±0.441) 0.5561(±0.610) 0.5605(±0.633) 0.5089(±0.754) 0.5113(±0.779)
TLR 0.5237(±0.356) 0.5277(±0.301) 0.5073(±0.417) 0.5082(±0.485) 0.5117(±0.527) 0.5098(±0.533)
TSVM 0.5488(±0.536) 0.5392(±0.467) 0.5462(±0.538) 0.5433(±0.561) 0.5291(±0.758) 0.5232(±0.685)
TCA 0.7821(±1.527) 0.7847(±1.538) 0.7154(±1.475) 0.6816(±1.423) 0.7782(±1.644) 0.7866(±1.595)
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