A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks
There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/5/1603 |
| _version_ | 1818777637182504960 |
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| author | Jinli Zhang Tong Li Zongli Jiang Xiaohua Hu Ali Jazayeri |
| author_facet | Jinli Zhang Tong Li Zongli Jiang Xiaohua Hu Ali Jazayeri |
| author_sort | Jinli Zhang |
| collection | DOAJ |
| description | There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms. |
| first_indexed | 2024-12-18T11:31:59Z |
| format | Article |
| id | doaj.art-f0fe2f23e7554d1da300f64a00472b08 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-18T11:31:59Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f0fe2f23e7554d1da300f64a00472b082022-12-21T21:09:35ZengMDPI AGApplied Sciences2076-34172020-02-01105160310.3390/app10051603app10051603A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information NetworksJinli Zhang0Tong Li1Zongli Jiang2Xiaohua Hu3Ali Jazayeri4Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaCollege of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USACollege of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USAThere has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.https://www.mdpi.com/2076-3417/10/5/1603heterogeneous information networksclassificationmeta graphmeta path |
| spellingShingle | Jinli Zhang Tong Li Zongli Jiang Xiaohua Hu Ali Jazayeri A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks Applied Sciences heterogeneous information networks classification meta graph meta path |
| title | A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks |
| title_full | A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks |
| title_fullStr | A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks |
| title_full_unstemmed | A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks |
| title_short | A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks |
| title_sort | noval weighted meta graph method for classification in heterogeneous information networks |
| topic | heterogeneous information networks classification meta graph meta path |
| url | https://www.mdpi.com/2076-3417/10/5/1603 |
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