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数据分析与知识发现  2018, Vol. 2 Issue (9): 88-99     https://doi.org/10.11925/infotech.2096-3467.2018.0342
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于迁移成分分析的多层社交网络链接分类*
伍杰华1,2(), 沈静1, 周蓓1
1广东工贸职业技术学院计算机与信息工程学院 广州 510510
2华南理工大学计算机科学与工程学院 广州 510641
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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摘要 

【目的】解决多层次社交网络链接分类算法无法有效获取各层次间子网络的关联信息, 从而提高分类性能的问题。【方法】定义反映各子网络间关联的共性特征和反映各子网络自身属性结构的特性特征, 提出一种基于迁移成分分析的多层次社交网络链接分类算法, 获取反映层次间相互关联特征的组件, 使得不同层次的子网络能够相互学习。【结果】通过在真实多层次数据集YouTube和QueryLog上与基准分类算法、基于特征学习的分类算法和基准迁移分类算法进行比较, 在AUC和ROC曲线的评价指标上有1.57%-33.2%的提升。【局限】未能处理超大规模的网络数据, 同时尚未深入探讨特征定义的维度和性能之间的关系。【结论】本文方法能够有效地将迁移学习思想应用到多层次社交网络链接分类场景, 为该类模型的研究提供一种新的方案。

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伍杰华
沈静
周蓓
关键词 多层次网络社交网络链接分类成分分析迁移学习    
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 wordsMultilayer Network    Social Network    Link Classification    Component Analysis    Transfer Learning
收稿日期: 2018-03-28      出版日期: 2018-10-25
ZTFLH:  分类号: TP391 G35  
基金资助:*本文系广东省科技计划项目“大规模异构复杂网络链接预测理论/算法和应用研究”(项目编号: 2017ZC0348)、广东省高等学校优秀青年教师培养计划项目“大规模异构复杂网络链接预测理论的研究和应用”(项目编号: YQ2015177)和广东高校重大科研项目与成果培育计划项目“多源异质网络链接预测关键技术研究”(项目编号: 2017GKTSCX009)的研究成果之一
引用本文:   
伍杰华, 沈静, 周蓓. 基于迁移成分分析的多层社交网络链接分类*[J]. 数据分析与知识发现, 2018, 2(9): 88-99.
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.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0342      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I9/88
  多层次社交网络结构及算法流程
  YouTube数据集中异构链接分布
  YouTube数据集中共邻节点和链接关系
  子网络度分布
  子网络聚类系数分布
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
  多层网络结构属性
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)
  YouTube数据集下各模型的分类结果(标准差基准单位是10-3)
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)
  QueryLog数据集下各模型的分类结果(标准差基准单位是10-3)
  YouTube案例下的ROC曲线图
  QueryLog案例下的ROC曲线图
  不同规模源网络下各迁移案例的分类效果
  不同特征下各案例的分类效果
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