FEGR: Feature Enhanced Graph Representation Method for Graph Classification-Reference-Cited by-同舟云学术

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FEGR: Feature Enhanced Graph Representation Method for Graph Classification

Published:2023-11-06 Issue: Volume:7 Page:371-378
ISSN:
Container-title:Proceedings of the International Conference on Advances in Social Networks Analysis and Mining
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Author:

Abushofa Mohamad Elhadi¹^ORCID,Atapour Abarghouei Amir²^ORCID,Forshaw Matthew³^ORCID,Mcgough Andrew Stephen⁴^ORCID

Affiliation:

1. Computing School, PhD Student at Newcastle University, Newcastle upon Tyne, United Kingdom

2. Computer science, Assistant Professor at Durham University, Durham, United Kingdom

3. Computing School, Reader in Data Science at Newcastle University, Newcastle upon Tyne, United Kingdom

4. Computing School, Reader in Machine Learning at Newcastle University, Newcastle upon Tyne, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3625007.3627600

Reference55 articles.

1. DGSD: Distributed graph representation via graph statistical properties

2. Graph Representation Learning

3. Mining network-level properties of Twitter altmetrics data

4. A. Said, S.-U. Hassan, W. Abbas, and M. Shabbir, "Netki: A kirchhoff index based statistical graph embedding in nearly linear time," Neurocomputing, 2020.

5. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, "A comprehensive survey on graph neural networks," IEEE transactions on neural networks and learning systems, vol. 32, no. 1, pp. 4--24, 2020.

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