Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (9): 88-99    DOI: 10.11925/infotech.2096-3467.2018.0342
Current Issue | Archive | Adv Search |
Classifying Multilayer Social Network Links Based on Transfer Component Analysis
Jiehua Wu1,2(),Jing Shen1,Bei Zhou1
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
Download: PDF(980 KB)   HTML ( 2
Export: BibTeX | EndNote (RIS)      

[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     
Received: 28 March 2018      Published: 25 October 2018

Cite this article:

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

URL:     OR

[1] Kim J, Hastak M.Social Network Analysis: Characteristics of Online Social Networks After a Disaster[J]. International Journal of Information Management, 2018, 38(1): 86-96.
[2] 林学民, 杜小勇, 李翠平. 基于图结构的大数据分析与管理技术专刊前言[J]. 软件学报, 2018, 29(3): 525-527.
[2] (Lin Xuemin, Du Xiaoyong, Li Cuiping.Preface to Special Issue of Big Data Analysis and Management Technology Based on Graph Structure[J]. Journal of Software, 2018, 29(3): 525-527.)
[3] Zafarani R, Abbasi M A, Liu H.Social Media Mining: An Introduction[M]. New York: Cambridge University Press, 2014.
[4] Wang P, Xu B W, Wu Y R, et al.Link Prediction in Social Networks: The State-of-the-Art[J]. Science China: Information Sciences, 2015, 58(1): 1-38.
[5] Enugala R, Rajamani L, Kurapati S, et al.Detecting Communities in Dynamic Social Networks Using Modularity Ensembles SOM[J]. International Journal of Rough Sets and Data Analysis (IJRSDA), 2018, 5(1): 34-43.
[6] Huang X, Li J, Hu X.Label Informed Attributed Network Embedding[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 2017: 731-739.
[7] Dai C, Chen L, Li B, et al. Link Prediction in Multi-relational Networks Based on Relational Similarity[J]. Information Sciences, 2017, 394-395: 198-216.
[8] Lü L, Zhou T.Link Prediction in Complex Networks: A Survey[J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(6):1150-1170.
[9] Hasan M A, Chaoji V, Salem S, et al.Link Prediction Using Supervised Learning[C]//Proceedings of the 2006 SDM Workshop on Link Analysis, Counterterrorism and Security. 2006.
[10] Ma C, Bao Z K, Zhang H F.Improving Link Prediction in Complex Networks by Adaptively Exploiting Multiple Structural Features of Networks[OL]. arXiv Preprint, arXiv: 1608.04533.
[11] Tang J, Lou T, Kleinberg J, et al. Transfer Learning to Infer Social Ties Across Heterogeneous Networks[J].ACM Transactions on Information Systems (TOIS), 2016, 34(2): Article No.7.
[12] Scellato S, Noulas A, Mascolo C.Exploiting Place Features in Link Prediction on Location-based Social Networks[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2011: 1046-1054.
[13] Boccaletti S, Bianconi G, Criado R, et al.The Structure and Dynamics of Multilayer Networks[J]. Physics Reports, 2014, 544(1): 1-122.
[14] Hristova D, Noulas A, Brown C, et al.A Multilayer Approach to Multiplexity and Link Prediction in Online Geo-social Networks[J]. EPJ Data Science, 2016, 5:24.
[15] Yang Y, Chawla N, Sun Y, et al.Predicting Links in Multi-relational and Heterogeneous Networks[C]// Proceedings of the 12th International Conference on Data Mining. 2012.
[16] Sun Y, Barber R, Gupta M, et al.Co-author Relationship Prediction in Heterogeneous Bibliographic Networks[C]// Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining. 2011.
[17] Pan S J, Yang Q.A Survey on Transfer Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359.
[18] Pan S J, Tsang I W, Kwok J T, et al.Domain Adaptation via Transfer Component Analysis[J]. IEEE Transactions on Neural Networks, 2011, 22(2): 199-210.
[19] Borgwardt K M, Gretton A, Rasch M J, et al.Integrating Structured Biological Data by Kernel Maximum Mean Discrepancy[J]. Bioinformatics, 2006, 22(14): 49-57.
[20] Narasimhan J, Holder L.Feature Engineering for Supervised Link Prediction on Dynamic Social Networks[OL]. arXiv Preprint, arXiv: 1410.1783.
[21] Lü L, Chen D, Ren X L, et al.Vital Nodes Identification in Complex Networks[J]. Physics Reports, 2016, 650: 1-63.
[22] Kleinberg J M.Authoritative Sources in a Hyperlinked Environment[J]. Journal of the ACM, 1999, 46(5): 604-632.
[23] Page L. The PageRank Citation Ranking: Bringing Order to the Web[R/OL]. Stanford InfoLab. .
[24] Ma?kiewicz A, Ratajczak W.Principal Components Analysis[J]. Computers & Geosciences, 1993, 19(3): 303-342.
[25] Berlingerio M, Coscia M, Giannotti F, et al.Foundations of Multidimensional Network Analysis[C]//Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2011:485-489.
[26] 伍杰华. 基于RReliefF特征选择算法的复杂网络链接分类[J]. 计算机工程, 2017, 43(8):208-214.
[26] (Wu Jiehua.Complex Network Link Classification Based on RReliefF Feature Selection Algorithm[J]. Computer Engineering, 2017, 43(8): 208-214.)
[1] Liqing Qiu,Wei Jia,Xin Fan. Influence Maximization Algorithm Based on Overlapping Community[J]. 数据分析与知识发现, 2019, 3(7): 94-102.
[2] Xiaolan Wu,Chengzhi Zhang. Analysis of Knowledge Flow Based on Academic Social Networks:
A Case Study of
[J]. 数据分析与知识发现, 2019, 3(4): 107-116.
[3] Guanghui Ye,Jinglan Hu,Jian Xu,Lixin Xia. Analyzing Growth Trends and Attachment Mode of Social Blog Tags[J]. 数据分析与知识发现, 2018, 2(6): 70-78.
[4] Bo Guo,Junrui Zhao,Yu Sun. Analyzing Characteristics and Dynamics of User Behaviors in Social Q&A Community: Case Study of[J]. 数据分析与知识发现, 2018, 2(4): 48-58.
[5] Yuan Chen,Chaoqun Wang,Zhongyi Hu,Jiang Wu. Identifying Malicious Websites with PCA and Random Forest Methods[J]. 数据分析与知识发现, 2018, 2(4): 71-80.
[6] Feifei Wang,Shengtai Zhang. Analyzing Information Behaviors of Mobile Social Network Users[J]. 数据分析与知识发现, 2018, 2(4): 99-109.
[7] Ling Zhang,Manman Luo,Lijun Zhu. Analyzing Information Dissemination on Social Networks[J]. 数据分析与知识发现, 2018, 2(2): 46-57.
[8] Fen Chen,Xi Fu,Yuan He,Chunxiang Xue. Identifying Weibo Opinion Leaders with Social Network Analysis and Influence Diffusion Model[J]. 数据分析与知识发现, 2018, 2(12): 60-67.
[9] Gang Li,Xiao Wang,Yang Guo. Detecting Relationship Among WeChat Group Members with Co-occurrence of Cooperation[J]. 数据分析与知识发现, 2018, 2(11): 54-63.
[10] Zhongyi Wang,Heming Zhang,Jing Huang,Chunya Li. Studying Knowledge Dissemination of Online Q&A Community with Social Network Analysis[J]. 数据分析与知识发现, 2018, 2(11): 80-94.
[11] Zhen Li,Shengchun Ding,Nan Wang. Identifying Topics of Online Public Opinion[J]. 数据分析与知识发现, 2017, 1(8): 18-30.
[12] Chuanming Yu,Bolin Feng,Lu An. Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning[J]. 数据分析与知识发现, 2017, 1(7): 73-81.
[13] Fei Li,Jian Zhang,Zongshui Wang. Review of Social Recommendation with Bibliometrics and Social Network Analysis[J]. 数据分析与知识发现, 2017, 1(6): 22-35.
[14] Xiwei Wang,Liu Zhang,Shimeng Li,Nan’axue Wang. The Dissemination of Online Public Opinion on Social Welfare Issues via New Media: Case Study of “Draw up the Lifeline” in Sina Weibo[J]. 数据分析与知识发现, 2017, 1(6): 93-101.
[15] Ruxia Fan,Jianxun Zeng,Yaruixi Gao. Recognizing Dynamic Academic Impacts of Scholars Based on Cooperative Network[J]. 数据分析与知识发现, 2017, 1(4): 30-37.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938