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Journal article
A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks
Applied sciences, v 10(5), p1603
01 Mar 2020
Abstract
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.
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Details
- Title
- A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks
- Creators
- Jinli Zhang - Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R ChinaTong Li - Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R ChinaZongli Jiang - Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R ChinaXiaohua Hu - Drexel UniversityAli Jazayeri - Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA
- Publication Details
- Applied sciences, v 10(5), p1603
- Publisher
- Mdpi
- Number of pages
- 14
- Grant note
- 1744661 / NSF IIS; National Science Foundation (NSF) China Scholarship Council
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000525298100051
- Scopus ID
- 2-s2.0-85082487893
- Other Identifier
- 991019167711704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Multidisciplinary
- Engineering, Multidisciplinary
- Materials Science, Multidisciplinary
- Physics, Applied