The correlation between the complexities of the non-hierarchical and hierarchical versions of graph problems

Dual parameterization and parameterized approximability of subset graph problems

RAIRO - Operations Research ◽

10.1051/ro/2016018 ◽

2017 ◽

Vol 51 (1) ◽

pp. 261-266 ◽

Cited By ~ 1

Author(s):

Édouard Bonnet ◽

Vangelis Th. Paschos

Keyword(s):

Graph Problems

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Heuristics for Semirandom Graph Problems

Journal of Computer and System Sciences ◽

10.1006/jcss.2001.1773 ◽

2001 ◽

Vol 63 (4) ◽

pp. 639-671 ◽

Cited By ~ 58

Author(s):

Uriel Feige ◽

Joe Kilian

Keyword(s):

Graph Problems

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Multistage graph problems on a global budget

Theoretical Computer Science ◽

10.1016/j.tcs.2021.04.002 ◽

2021 ◽

Vol 868 ◽

pp. 46-64

Author(s):

Klaus Heeger ◽

Anne-Sophie Himmel ◽

Frank Kammer ◽

Rolf Niedermeier ◽

Malte Renken ◽

...

Keyword(s):

Global Budget ◽

Graph Problems

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Efficient parallel algorithms for some graph problems

Communications of the ACM ◽

10.1145/358628.358650 ◽

1982 ◽

Vol 25 (9) ◽

pp. 659-665 ◽

Cited By ~ 130

Author(s):

Francis Y. Chin ◽

John Lam ◽

I-Ngo Chen

Keyword(s):

Parallel Algorithms ◽

Graph Problems

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Newton's method for constrained norm minimization and its application to weighted graph problems

2014 American Control Conference ◽

10.1109/acc.2014.6858611 ◽

2014 ◽

Cited By ~ 2

Author(s):

Mahmoud El Chamie ◽

Giovanni Neglia

Keyword(s):

Newton’S Method ◽

Newton's Method ◽

Weighted Graph ◽

Graph Problems ◽

Norm Minimization

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MetaLearning with Graph Neural Networks

ACM SIGKDD Explorations Newsletter ◽

10.1145/3510374.3510379 ◽

2021 ◽

Vol 23 (2) ◽

pp. 13-22

Author(s):

Debmalya Mandal ◽

Sourav Medya ◽

Brian Uzzi ◽

Charu Aggarwal

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Future Research ◽

Open Problems ◽

Research Directions ◽

Graph Problems ◽

Meta Learning ◽

Comprehensive Survey ◽

Future Research Directions ◽

Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

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