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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Learning Structural Node Representation Using Graph Kernels |
| Authors Name: | Dr.K.SEKAR , S.LATHA RANI |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_206426
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| Published Paper Id: | IJNRD2310073 |
| Published In: | Volume 8 Issue 10, October-2023 |
| DOI: | |
| Abstract: | Graphs are a ubiquitous data structure used to represent relationships and dependencies in various domains, including social networks, biology, and recommendation systems. Extracting meaningful representations of nodes within these graphs is essential for tasks such as node classification, link prediction, and community detection. Traditional methods often rely on handcrafted features or heuristics, which may not capture the complex structural patterns present in the data. In this study, we propose a novel approach for learning structural node representations using graph kernels. Graph kernels are powerful tools that quantify the similarity between graphs by comparing their substructures. We extend this concept to capture the structural information of individual nodes within a graph. Our approach leverages graph kernels to generate node embeddings that encode the local and global structural context surrounding each node. To evaluate the effectiveness of our method, we conduct experiments on a diverse set of real-world graph datasets, spanning social networks, biological networks, and citation networks. The results demonstrate that our approach outperforms traditional methods and achieves state-of-the-art performance on various node-centric tasks. Furthermore, we provide insights into the interpretability of the learned node representations, shedding light on the structural patterns and relationships captured by our method. These interpretable representations have the potential to enhance our understanding of graph data and facilitate downstream applications in various domains. In summary, our work introduces a novel framework for learning structural node representations using graph kernels, offering a powerful and interpretable approach for analyzing and extracting valuable insights from graph-structured data. This research contributes to the growing field of graph representation learning and holds promise for a wide range of applications in network analysis, recommendation systems, and beyond. |
| Keywords: | Structural Learning, Node Representation, graph kernels, structural patterns and graph-structured data |
| Cite Article: | "Learning Structural Node Representation Using Graph Kernels", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 10, page no.a659-a670, October-2023, Available :http://www.ijnrd.org/papers/IJNRD2310073.pdf |
| Downloads: | 000118756 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2310073 Registration ID: 206426 Published In: Volume 8 Issue 10, October-2023 DOI (Digital Object Identifier): Page No: a659-a670 Country: Chennai/Tamil Nadu, TamilNadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2310073 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2310073 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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