Node Influence Based Similarity Measure Method in Heterogeneous Network

被引：0
|
作者：
Liu L. [1 ,2 ,3 ,4 ]
Hu F.-Y. [4 ]
Niu L. [5 ]
Peng T. [1 ,2 ,3 ]
机构：
[1] College of Software, Jilin University, Changchun, 130012, Jilin
[2] Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Ministry of Education, Changchun, 130012, Jilin
[3] College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin
[4] College of Communication Engineering, Jilin University, Changchun, 130012, Jilin
[5] The First Hospital of Jilin university, Changchun, 130012, Jilin
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 09期
关键词：
Data mining; Heterogeneous network; Knowledge graph; Link structure; Network search; Node influence; Recommended system; Semantic relationship;
D O I：
10.3969/j.issn.0372-2112.2019.09.016
中图分类号：
学科分类号：
摘要：
Heterogeneous network similarity learning is to analyze the degree of correlation between two different types of objects. Different types of objects have different degrees of importance in heterogeneous networks, and play different roles in the similarity learning process. This paper proposes a node influence based similarity measure method (NISim) heterogeneous information network. This method not only considers the link structure in network but also keeps the semantic information in heterogeneous networks. Also, this method distinguishes the effect to heterogeneous network brought by different types of nodes. In heterogeneous network, the heuristic rules are used to distinguish and quantify the influence weight of different types of nodes. In addition, the link structure in network and the semantic relationship are combined to solve the problem of improving similarity learning accuracy. Experimental results show that this method can measure the similarity between different types of nodes effectively. It can be applied in different fields such as network search, recommendation system and knowledge graph construction and so on. © 2019, Chinese Institute of Electronics. All right reserved.
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页码：1929 / 1936
页数：7
相关论文
共 24 条
  • [1] Wei Y., Song Y.Q., Zhen Y., Liu B., Yang Q., Heterogeneous translated hashing: A scalable solution towards multi-modal similarity search, ACM Transactions on Knowledge Discovery from Data, 10, 4, (2016)
  • [2] Nguyen P., Wang J., Hilario M., Kalousis A., Learning heterogeneous similarity measures for hybrid recommendations in meta-mining, Proceedings of the 12th IEEE International Conference on Data Mining, pp. 1026-1031, (2012)
  • [3] Wu J., Shen H., Li Y.D., Xiao Z.B., Lu M.Y., Wang C.L., Learning a hybrid similarity measure for image retrieval, Pattern Recognition, 46, 11, pp. 2927-2939, (2013)
  • [4] Lin Y.K., Liu Z.Y., Sun M.S., Liu Y., Zhu X., Learning entity and relation embeddings for knowledge graph completion, Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2181-2187, (2015)
  • [5] Wang C.G., Song Y.Q., Li H.R., Zhang M., Han J.W., KnowSim: A document similarity measure on structured heterogeneous information networks, Proceedings of IEEE International Conference on Data Mining, pp. 1015-1020, (2015)
  • [6] Mehdi O., Ibrahim H., Affendey L., An approach for instance based schema matching with Google similarity and regular expression, International Arab Journal of Information Technology, 14, 5, pp. 755-763, (2017)
  • [7] Junejo K.N., Karim A., Hassan M.T., Jeon M., Terms-based discriminative information space for robust text classification, Information Sciences, 372, pp. 518-538, (2016)
  • [8] Tripathy A., Anand A., Rath S.K., Document-level sentiment classification using hybrid machine learning approach, Knowledge and Information Systems, 53, 3, pp. 805-831, (2017)
  • [9] Zhang Y.L., Li C.P., Xie C.W., Chen H., Accuracy estimation of link-based similarity measures and its application, Frontiers of Computer Science, 10, 1, pp. 113-123, (2016)
  • [10] Hamedani M.R., Kim S.W., JacSim: An accurate and efficient link-based similarity measure in graphs, Information Sciences, 414, pp. 203-224, (2017)
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