Frequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so far has considered the structural differences of vertices and their labels. Discerning how to identify cases that differ from the initial pattern by any number of vertices, edges, or labels has become the main challenge of recent research works. As a solution, we suggest a novel FMP algorithm named mining frequent approximate patterns (MFAPs) with two central new characteristics. First, we begin by using the inexact matching technique, which allows for structural differences in edge, vertices, and labels. Second, we follow the approximate matching with a focus on mining patterns within the directed graph, as opposed to the more commonly explored case of patterns being mined from the undirected graph. Our results illustrate the effectiveness of this new MFAP algorithm in identifying patterns within an optimized time.

中文翻译：

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在大型网络中挖掘频繁的近似模式

频繁模式挖掘 (FPM) 算法通常基于图同构，以识别常见的模式出现。然而，最近的研究工作集中在模式可能与其发生的情况不同的情况上。这样的案例对于分析嘈杂的网络数据具有很大的潜力。大多数现有的 FPM 算法都考虑了边及其标签的差异，但到目前为止，它们都没有考虑顶点及其标签的结构差异。辨别如何通过任意数量的顶点、边或标签识别与初始模式不同的案例已成为最近研究工作的主要挑战。作为解决方案，我们提出了一种名为挖掘频繁近似模式 (MFAP) 的新型 FMP 算法，该算法具有两个核心新特征。首先，我们从使用不精确匹配技术开始，这允许边缘、顶点和标签的结构差异。其次，我们遵循近似匹配，重点是在有向图中挖掘模式，而不是从无向图中挖掘模式的更常见的探索情况。我们的结果说明了这种新的 MFAP 算法在优化时间内识别模式的有效性。