Mr. Shilong Lin | Computer Science | Best Researcher Award
Mr. Shilong Lin | Computer Science | Guangxi Normal University | China
Mr Shilong Lin is a dedicated researcher in computer science, specializing in graph neural networks, representation learning, and advanced machine learning methods, and he is currently affiliated with the School of Computer Science and Engineering at Guangxi Normal University, where he is engaged in both academic research and teaching activities; Mr Shilong Lin has pursued his education within the same institution, building a strong foundation in artificial intelligence, machine learning, and data-driven modeling, and his academic journey has enabled him to develop deep expertise in designing algorithms for node classification, graph similarity learning, contrastive learning, and adaptive feature fusion; throughout his professional experience, Mr Shilong Lin has collaborated with notable international scholars such as Guangquan Lu, Longqing Du, Shichao Zhang, Cuifang Zou, and Xuxia Zeng, contributing to impactful research projects that address challenges in imbalanced data learning, multi-hop contrastive approaches, multi-scale graph feature fusion, and cross-level interaction modeling; his research interests span graph representation learning, deep learning architectures, imbalance-aware modeling, scalable graph algorithms, and AI-driven knowledge discovery, and these interests have shaped his contribution to several high-quality journal and conference publications across reputed platforms including Neurocomputing, Information Processing & Management, and the International Joint Conference on Artificial Intelligence (IJCAI); in terms of research skills, Mr Shilong Lin demonstrates strong capabilities in algorithm design, graph neural network development, experimental evaluation, data preprocessing, model optimization, and collaborative scientific writing, supported by his competence in programming, machine learning frameworks, and scientific communication; while building his research trajectory, Mr Shilong Lin has also contributed to academic community participation through teamwork, collaborative problem solving, and involvement in multi-author research projects that enhance the scientific visibility of his institution; his awards and honors reflect his ongoing contribution to high-quality research, including recognition for publications in top-tier AI and computational intelligence venues and his involvement in internationally indexed works listed under ORCID; overall, Mr Shilong Lin continues to advance his research career with strong dedication, intellectual rigor, and a vision of contributing to the development of efficient, explainable, and scalable graph learning systems, and his growing academic output positions him as a promising scholar who is steadily building an impactful presence in the global artificial intelligence research community.
Academic Profile: ORCID
Featured Publications:
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Lin, S., Lu, G., Zhang, S., Du, L., & Luo, Z. (2025). Synthesizing stronger nodes for minority classes in imbalanced node classification.
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Zeng, X., Lu, G., Zou, C., Lin, S., Du, L., & Zhang, S. (2025). Multi-hop contrastive learning with feature augmentation for node classification.
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Zou, C., Lu, G., Zhang, W., Zeng, X., Lin, S., Du, L., & Zhang, S. (2025). Enhanced graph similarity learning via adaptive multi-scale feature fusion.
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Zou, C., Lu, G., Du, L., Zeng, X., & Lin, S. (2025). Graph similarity learning for cross-level interactions.