Diffusion Models for Cayley Graphs
We review the problem of finding paths in Cayley graphs of groups and group actions, using the Rubik's cube as an example, and we list several more examples of significant mathematical interest. We then show how to formulate these problems in the framework of diffusion models. The exploration of the graph is carried out by the forward process, while finding the target nodes is done by the inverse backward process. This systematizes the discussion and suggests many generalizations. To improve exploration, we propose a ``reversed score'' ansatz which substantially improves over previous comparable algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Rubik's CubeMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs
This paper is the second in a series of studies on developing efficient artificial intelligence-based approaches to pathfinding on extremely large graphs (e.g. $10^{70}$ nodes) with a focus on Cayley graphs and mathemati…
Benchmarkingreinforcement-learningReinforcement LearningCayleyPy Growth: Efficient growth computations and hundreds of new conjectures on Cayley graphs (Brief version)
This is the third paper of the CayleyPy project applying artificial intelligence to problems in group theory. We announce the first public release of CayleyPy, an open source Python library for computations with Cayley a…
A General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks
A Graph Neural Network (GNN) framework for predicting the solvability of finite groups from their Cayley graph representations was introduced in [1]. In the present work, we generalize this approach and develop a propert…
Graph Representation LearningGraph Neural NetworkSignal processing on large networks with group symmetries
Current methods of graph signal processing rely heavily on the specific structure of the underlying network: the shift operator and the graph Fourier transform are both derived directly from a specific graph. In many cas…
Stochastic Block ModelLearning the Graphical Nature of Symmetries
Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured, compared, and learned. In this paper, a dataset of $131{,}406$ Cayley…
Graph Neural Network