DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification
This paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance. Unlike existing methods that treat these two paradigms separately, our approach integrates...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/3/322 |
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| author | Xin Xu Xinya Lu Jianan Wang |
| author_facet | Xin Xu Xinya Lu Jianan Wang |
| author_sort | Xin Xu |
| collection | DOAJ |
| description | This paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance. Unlike existing methods that treat these two paradigms separately, our approach integrates them into a cohesive model, addressing limitations in structural feature extraction and neighborhood relationship modeling. DeeWaNA first leverages DeepWalk to capture global structural information and then employs an attention-based weighting mechanism to refine neighborhood relationships through a novel distance metric. Finally, a weighted aggregation operator fuses these representations into a unified low-dimensional space. By bridging the gap between random-walk-based and neural-network-based techniques, our framework enhances representation quality and improves classification accuracy. Extensive evaluations on real-world networks demonstrate that DeeWaNA outperforms four widely used unsupervised network representation learning methods, underscoring its effectiveness and broader applicability. |
| format | Article |
| id | doaj-art-4976c645894f4b4f8765c14064e4a75e |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-4976c645894f4b4f8765c14064e4a75e2025-08-20T02:11:14ZengMDPI AGEntropy1099-43002025-03-0127332210.3390/e27030322DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node ClassificationXin Xu0Xinya Lu1Jianan Wang2School of Media Science, Northeast Normal University, Jingye Street 2555, Changchun 130117, ChinaSchool of Information Science and Technology, Northeast Normal University, Jingye Street 2555, Changchun 130117, ChinaSchool of Physics, Northeast Normal University, Renmin Street 5268, Changchun 130024, ChinaThis paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance. Unlike existing methods that treat these two paradigms separately, our approach integrates them into a cohesive model, addressing limitations in structural feature extraction and neighborhood relationship modeling. DeeWaNA first leverages DeepWalk to capture global structural information and then employs an attention-based weighting mechanism to refine neighborhood relationships through a novel distance metric. Finally, a weighted aggregation operator fuses these representations into a unified low-dimensional space. By bridging the gap between random-walk-based and neural-network-based techniques, our framework enhances representation quality and improves classification accuracy. Extensive evaluations on real-world networks demonstrate that DeeWaNA outperforms four widely used unsupervised network representation learning methods, underscoring its effectiveness and broader applicability.https://www.mdpi.com/1099-4300/27/3/322unsupervised network representation learningnode classificationrandom walkneighborhood aggregationgraph embedding |
| spellingShingle | Xin Xu Xinya Lu Jianan Wang DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification Entropy unsupervised network representation learning node classification random walk neighborhood aggregation graph embedding |
| title | DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification |
| title_full | DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification |
| title_fullStr | DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification |
| title_full_unstemmed | DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification |
| title_short | DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification |
| title_sort | deewana an unsupervised network representation learning framework integrating deepwalk and neighborhood aggregation for node classification |
| topic | unsupervised network representation learning node classification random walk neighborhood aggregation graph embedding |
| url | https://www.mdpi.com/1099-4300/27/3/322 |
| work_keys_str_mv | AT xinxu deewanaanunsupervisednetworkrepresentationlearningframeworkintegratingdeepwalkandneighborhoodaggregationfornodeclassification AT xinyalu deewanaanunsupervisednetworkrepresentationlearningframeworkintegratingdeepwalkandneighborhoodaggregationfornodeclassification AT jiananwang deewanaanunsupervisednetworkrepresentationlearningframeworkintegratingdeepwalkandneighborhoodaggregationfornodeclassification |