Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle
Link prediction for temporal heterogeneous networks is an important task in the field of network science, and it has a wide range of real-world applications. Traditional link prediction methods are mainly based on static homogeneous networks, which do not distinguish between different types of nodes...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/16/3541 |
| _version_ | 1827729264010592256 |
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| author | Jiaping Cao Jichao Li Jiang Jiang |
| author_facet | Jiaping Cao Jichao Li Jiang Jiang |
| author_sort | Jiaping Cao |
| collection | DOAJ |
| description | Link prediction for temporal heterogeneous networks is an important task in the field of network science, and it has a wide range of real-world applications. Traditional link prediction methods are mainly based on static homogeneous networks, which do not distinguish between different types of nodes in the real world and do not account for network structure evolution over time. To address these issues, in this paper, we study the link prediction problem in temporal heterogeneous networks and propose a link prediction method for temporal heterogeneous networks (LP-THN) based on the information lifecycle, which is an end-to-end encoder–decoder structure. The information lifecycle accounts for the active, decay and stable states of edges. Specifically, we first introduce the meta-path augmented residual information matrix to preserve the structure evolution mechanism and semantics in HINs, using it as input to the encoder to obtain a low-dimensional embedding representation of the nodes. Finally, the link prediction problem is considered a binary classification problem, and the decoder is utilized for link prediction. Our prediction process accounts for both network structure and semantic changes using meta-path augmented residual information matrix perturbations. Our experiments demonstrate that LP-THN outperforms other baselines in both prediction effectiveness and prediction efficiency. |
| first_indexed | 2024-03-10T23:45:31Z |
| format | Article |
| id | doaj.art-52befe76b405461d9e48c34b377e8f08 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T23:45:31Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-52befe76b405461d9e48c34b377e8f082023-11-19T02:03:34ZengMDPI AGMathematics2227-73902023-08-011116354110.3390/math11163541Link Prediction for Temporal Heterogeneous Networks Based on the Information LifecycleJiaping Cao0Jichao Li1Jiang Jiang2College of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaLink prediction for temporal heterogeneous networks is an important task in the field of network science, and it has a wide range of real-world applications. Traditional link prediction methods are mainly based on static homogeneous networks, which do not distinguish between different types of nodes in the real world and do not account for network structure evolution over time. To address these issues, in this paper, we study the link prediction problem in temporal heterogeneous networks and propose a link prediction method for temporal heterogeneous networks (LP-THN) based on the information lifecycle, which is an end-to-end encoder–decoder structure. The information lifecycle accounts for the active, decay and stable states of edges. Specifically, we first introduce the meta-path augmented residual information matrix to preserve the structure evolution mechanism and semantics in HINs, using it as input to the encoder to obtain a low-dimensional embedding representation of the nodes. Finally, the link prediction problem is considered a binary classification problem, and the decoder is utilized for link prediction. Our prediction process accounts for both network structure and semantic changes using meta-path augmented residual information matrix perturbations. Our experiments demonstrate that LP-THN outperforms other baselines in both prediction effectiveness and prediction efficiency.https://www.mdpi.com/2227-7390/11/16/3541temporal heterogeneous networkslink predictioninformation lifecyclemeta-path |
| spellingShingle | Jiaping Cao Jichao Li Jiang Jiang Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle Mathematics temporal heterogeneous networks link prediction information lifecycle meta-path |
| title | Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle |
| title_full | Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle |
| title_fullStr | Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle |
| title_full_unstemmed | Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle |
| title_short | Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle |
| title_sort | link prediction for temporal heterogeneous networks based on the information lifecycle |
| topic | temporal heterogeneous networks link prediction information lifecycle meta-path |
| url | https://www.mdpi.com/2227-7390/11/16/3541 |
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