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LGM: Mining Frequent Subgraphs from Linear Graphs

机译:LGM:从线性图形采集频繁的子图

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A linear graph is a graph whose vertices are totally ordered. Biological and linguistic sequences with interactions among symbols are naturally represented as linear graphs. Examples include protein contact maps, RNA secondary structures and predicate-argument structures. Our algorithm, linear graph miner (LGM), leverages the vertex order for efficient enumeration of frequent subgraphs. Based on the reverse search principle, the pattern space is systematically traversed without expensive duplication checking. Disconnected subgraph patterns are particularly important in linear graphs due to their sequential nature. Unlike conventional graph mining algorithms detecting connected patterns only, LGM can detect disconnected patterns as well. The utility and efficiency of LGM are demonstrated in experiments on protein contact maps.
机译:线性图形是其顶点完全有序的图形。符号之间的相互作用的生物和语言序列自然表示为线性图。实例包括蛋白质联系地图,RNA二次结构和谓词参数结构。我们的算法线性图形矿工(LGM),利用顶点顺序进行高效枚举频繁子图。基于反向搜索原理,在没有昂贵的重复检查的情况下系统地遍历模式空间。由于它们的顺序性质,断开的子图形模式在线性图表中尤为重要。与仅传统的图形挖掘算法不同,仅LGM也可以检测断开的图案。 LGM的实用性和效率在蛋白质联系地图上进行了实验。

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