Summary of A Short Review on Novel Approaches For Maximum Clique Problem: From Classical Algorithms to Graph Neural Networks and Quantum Algorithms, by Raffaele Marino et al.
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
by Raffaele Marino, Lorenzo Buffoni, Bogdan Zavalnij
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Optimization and Control (math.OC); Quantum Physics (quant-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides an exhaustive overview of the Maximum Clique Problem, which involves identifying subsets of vertices in a graph that are all pairwise adjacent to each other. The manuscript explores classic algorithms for solving this problem and includes a review of recent advancements in graph neural networks and quantum algorithms. Notably, the authors provide benchmarks for evaluating both classical and new learning-based as well as quantum algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how to find special groups of connected dots on a graph. Graphs are like big pictures made up of dots and lines that connect them. The problem is to find the biggest group of dots where every dot is connected to its neighbors. The paper looks at old ways of solving this problem and new ideas using computers and even quantum computing! It also shows how well these different methods work. |
