The maximum clique problem (MCP) is a famous NP-hard problem, which is difficult for the exact algorithm to solve when the dimension is large. In this paper, we applied the pointer network based method to solve this problem. First, we illustrated how to train the network with supervised learning strategy to obtain the solution to the maximum clique problem. We then further trained the pointer network with reinforcement learning strategy to obtain the vertices from the graph. For both strategies, backtracking algorithm is used to reselect the vertices. From the experimental results, we can see that both supervised learning and reinforcement learning work well. Promising results can be obtained up to 100 dimensions. This illustrates that the deep neural network based algorithms have great potentials for solving the maximum clique problem effectively and efficiently.

中文翻译：

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基于指针网络的最大团问题深度学习算法

最大团问题（MCP）是著名的NP-hard问题，当维度很大时，精确算法很难解决。在本文中，我们应用了基于指针网络的方法来解决这个问题。首先，我们说明了如何使用监督学习策略训练网络以获得最大团问题的解决方案。然后，我们使用强化学习策略进一步训练指针网络，以从图中获取顶点。对于这两种策略，回溯算法用于重新选择顶点。从实验结果可以看出，监督学习和强化学习都表现良好。可以在多达 100 个维度上获得有希望的结果。