SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs
Attributed graphs have an additional sign vector for each node. Typically, edge signs represent like or dislike relationship between the node pairs. This has applications in domains, such as recommender systems, personalised search, etc. However, limited availability of edge sign information in attr...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020033 |
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| author | Faima Abbasi Muhammad Muzammal Qiang Qu Farhan Riaz Jawad Ashraf |
| author_facet | Faima Abbasi Muhammad Muzammal Qiang Qu Farhan Riaz Jawad Ashraf |
| author_sort | Faima Abbasi |
| collection | DOAJ |
| description | Attributed graphs have an additional sign vector for each node. Typically, edge signs represent like or dislike relationship between the node pairs. This has applications in domains, such as recommender systems, personalised search, etc. However, limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge gap. Such inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the nodes. The study of attributed networks is challenging due to noise, sparsity, and class imbalance issues. In this work, we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed networks. We propose Semi-supervised Node Classification in Attributed graphs (SNCA). SNCA is robust to underlying network noise, and has in-built class imbalance handling capabilities. We perform an extensive experimental study on real-world datasets to showcase the efficiency, scalability, robustness, and pertinence of the solution. The performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings. |
| format | Article |
| id | doaj-art-c9b18aab5ace49698fb45e624bd6185c |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-c9b18aab5ace49698fb45e624bd6185c2025-02-03T11:53:25ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-09-017379480810.26599/BDMA.2024.9020033SNCA: Semi-Supervised Node Classification for Evolving Large Attributed GraphsFaima Abbasi0Muhammad Muzammal1Qiang Qu2Farhan Riaz3Jawad Ashraf4Luxembourg Institute of Science and Technology and University of Luxembourg, Esch-Sur-Alzette L-4362, LuxembourgDepartment of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UKShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSchool of Computer Science, University of Lincoln, Lincoln LN6 7TS, UKFaculty of Computing, Engineering and Media, De Montfort University, Leicester, LE1 9BH, UKAttributed graphs have an additional sign vector for each node. Typically, edge signs represent like or dislike relationship between the node pairs. This has applications in domains, such as recommender systems, personalised search, etc. However, limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge gap. Such inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the nodes. The study of attributed networks is challenging due to noise, sparsity, and class imbalance issues. In this work, we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed networks. We propose Semi-supervised Node Classification in Attributed graphs (SNCA). SNCA is robust to underlying network noise, and has in-built class imbalance handling capabilities. We perform an extensive experimental study on real-world datasets to showcase the efficiency, scalability, robustness, and pertinence of the solution. The performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.https://www.sciopen.com/article/10.26599/BDMA.2024.9020033attributed networksnode classificationrecommender systems |
| spellingShingle | Faima Abbasi Muhammad Muzammal Qiang Qu Farhan Riaz Jawad Ashraf SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs Big Data Mining and Analytics attributed networks node classification recommender systems |
| title | SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs |
| title_full | SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs |
| title_fullStr | SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs |
| title_full_unstemmed | SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs |
| title_short | SNCA: Semi-Supervised Node Classification for Evolving Large Attributed Graphs |
| title_sort | snca semi supervised node classification for evolving large attributed graphs |
| topic | attributed networks node classification recommender systems |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020033 |
| work_keys_str_mv | AT faimaabbasi sncasemisupervisednodeclassificationforevolvinglargeattributedgraphs AT muhammadmuzammal sncasemisupervisednodeclassificationforevolvinglargeattributedgraphs AT qiangqu sncasemisupervisednodeclassificationforevolvinglargeattributedgraphs AT farhanriaz sncasemisupervisednodeclassificationforevolvinglargeattributedgraphs AT jawadashraf sncasemisupervisednodeclassificationforevolvinglargeattributedgraphs |