Graph Learning Algorithm

A Graph Learning Algorithm is a graph creation algorithm that is a learning algorithm and can be applied by a graph learning system (to solve a graph learning task).

• AKA: Graph Learner, Network Learning Algorithm.
• Context:
  • …
• Example(s):
  • a Tree Learning Algorithm.
  • …
• Counter-Example(s):
  • a Statistical Model Learning Algorithm.
• See: Social Network Analysis, Graph-based Learning Algorithm.

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References

2023

• https://huggingface.co/blog/intro-graphml
  • QUOTE: The usual process to work on graphs with machine learning is first to generate a meaningful representation for your items of interest (nodes, edges, or full graphs depending on your task), then to use these to train a predictor for your target task. We want (as in other modalities) to constrain the mathematical representations of your objects so that similar objects are mathematically close. However, this similarity is hard to define strictly in graph ML: for example, are two nodes more similar when they have the same labels or the same neighbours?

2021

• (Xia et al., 2021) ⇒ F. Xia, K. Sun, S. Yu, A. Aziz, L. Wan, et al. (2021). “Graph Learning: A Survey." In: IEEE Transactions on ….
  • QUOTE: “Unlike text, audio and images, graph data are embedded in an irregular domain, making … machine learning algorithms inapplicable. Many graph learning models and algorithms have …”
  • NOTE: It provides a comprehensive review of graph learning models and their applicability in various domains.

2018

• (Qiao et al., 2018) ⇒ L. Qiao, L. Zhang, S. Chen, D. Shen. (2018). “Data-driven Graph Construction and Graph Learning: A Review." In: Neurocomputing.
  • QUOTE: “… machine learning algorithms (including semi-supervised learning, clustering, manifold … develop new graph learning models; (4) We discuss the relationship between graph learning and …”
  • NOTE: It highlights the importance of data-driven methods in graph learning and discusses their relationship to other machine learning methods.

2017

• (Zhan et al., 2017) ⇒ K. Zhan, C. Zhang, J. Guan, et al. (2017). “Graph Learning for Multiview Clustering." In: IEEE transactions on ….
  • QUOTE: “… Experiments are conducted on several benchmark datasets to verify the effectiveness and superiority of the proposed graph learning-based multiview clustering algorithm comparing to …”
  • NOTE: It focuses on multiview clustering and the benefits of using graph learning for this purpose.

2017

• (Egilmez et al., 2017) ⇒ H.E. Egilmez, E. Pavez, A. Ortega. (2017). “Graph Learning from Data under Laplacian and Structural Constraints." In: IEEE Journal of Selected ….
  • QUOTE: “… Since the main focus of the present paper is on formulation of graph learning problems and development of new algorithms, related applications are considered as part of our future work…”
  • NOTE: It emphasizes the need for considering Laplacian and structural constraints in graph learning.

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