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
[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.
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.
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