| 摘要: |
| 社交网络中的锚链识别对于跨网络信息传播、跨平台推荐、社交链预测等具有重要意义.针对当前锚链识别研究中准确率低的问题，提出了一种有效提高锚链识别准确率的方法：IAUE模型.该模型首先利用网络结构信息进行网络表征学习，然后利用BP神经网络、随机梯度下降和负采样等方法得到异构网络节点间的锚链候选集，最后辅以G-S算法精化锚链匹配结果，提高异构网络对齐的准确率.多个数据集上的实验结果表明，IAUE方法相比其他方法具有较高的性能和很好的泛化能力，可以较为准确地识别网络中的锚链. |
| 关键词: 表征学习 网络Embedding BP神经网络 网络对齐 G-S算法 |
| DOI： |
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| 基金项目:国家高技术研究发展计划（863）（2014AA015204）；山西省国际合作项目（201703D421013）；中国科学院计算技术研究所网络数据科学重点实验室课题（CASNDST20140X） |
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| Identifying Anchor Links on Social Networks Based on the IAUE Model |
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WANG Ning1, WANG Li2
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1.School of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600, China;2.School of Big Data, Taiyuan University of Technology, Jinzhong 030600, China
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| Abstract: |
| Identifying anchor links on social networks plays an important role in cross-network information dissemination, cross-platform recommendation, prediction of social chain, and so on. To improve the accuracy of anchor links identification, this paper proposes an effective method:the IAUE model. Firstly, the model uses network embedding algorithms to draw the network representation based on network structure. Then, a candidate set of matching nodes is gotten by BP neural network, stochastic gradient descent and negative sampling strategies. To refine the result of anchor links match, the G-S algorithm is used to reduce the useless information. Experiments upon multiple data sets show that the IAUE method has better performance and good generalization ability compared with other approaches. This research to some extent also can accurately identify the anchor links in the social network. |
| Key words: representation learning network embedding BP neural network network alignment G-S algorithm |
