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Clique and Constraint Models for Maximum Common (Connected) Subgraph Problems

机译:最大共同(连通)子图问题的集团模型和约束模型

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The maximum common subgraph problem is to find the largest subgraph common to two given graphs. This problem can be solved either by constraint-based search, or by reduction to the maximum clique problem. We evaluate these two models using modern algorithms, and see that the best choice depends mainly upon whether the graphs have labelled edges. We also study a variant of this problem where the subgraph is required to be connected. We introduce a filtering algorithm for this property and show that it may be combined with a restricted branching technique for the constraint-based approach. We show how to implement a similar branching technique in clique-inspired algorithms. Finally, we experimentally compare approaches for the connected version, and see again that the best choice depends on whether graphs have labels.
机译:最大公共子图问题是找到两个给定图公共的最大子图。此问题可以通过基于约束的搜索来解决,也可以通过简化为最大派系问题来解决。我们使用现代算法评估了这两个模型,发现最佳选择主要取决于图形是否带有标记的边缘。我们还研究了此问题的变体,其中需要连接子图。我们针对此属性引入了一种过滤算法,并表明它可以与基于约束的方法的受限分支技术结合使用。我们展示了如何在集团灵感算法中实现类似的分支技术。最后,我们实验性地比较了连接版本的方法,并再次看到最佳选择取决于图形是否具有标签。

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