基于用户实例的广义图模式挖掘
Mining Generalized Graph Patterns based on User Examples
摘要：
最近，人们对从图中挖掘模式产生了很大的兴趣。通常，感兴趣的模式的确切结构是未知的。例如，当挖掘分子结构以发现在化学化合物分类任务中用作特征的片段时，或者当挖掘网站以发现表示逻辑文档的网页集合时，就会发生这种情况。这种模式通常是根据某些泛化规则（GR）从几个小的子图（核心）中生成的。我们称这种模式为“广义模式”（GP）。虽然在结构上不同，但GP通常在网络中执行相同的功能。以前提出的方法来挖掘GP要么假设核心和GR是给定的，或者所有感兴趣的GP是频繁的。这些都是强有力的假设，在实际应用中往往不成立。在本文中，我们提出了一种方法来挖掘GP是免费的上述假设。给定用户选择的少量GP，我们的算法发现与用户示例相似的所有GP。首先，使用机器学习风格的方法来找到核。其次，计算图中的核心的泛化以识别GP。对生物和网络领域的真实核和GR生成的合成数据进行了评估，证明了我们方法的有效性。
英文摘要：
There has been a lot of recent interest in mining patterns from graphs. Often, the exact structure of the patterns of interest is not known. This happens, for example, when molecular structures are mined to discover fragments useful as features in chemical compound classification task, or when web sites are mined to discover sets of web pages representing logical documents. Such patterns are often generated from a few small subgraphs (cores), according to certain generalization rules (GRs). We call such patterns "generalized patterns"(GPs). While being structurally different, GPs often perform the same function in the network. Previously proposed approaches to mining GPs either assumed that the cores and the GRs are given, or that all interesting GPs are frequent. These are strong assumptions, which often do not hold in practical applications. In this paper, we propose an approach to mining GPs that is free from the above assumptions. Given a small number of GPs selected by the user, our algorithm discovers all GPs similar to the user examples. First, a machine learning-style approach is used to find the cores. Second, generalizations of the cores in the graph are computed to identify GPs. Evaluation on synthetic data, generated using real cores and GRs from biological and web domains, demonstrates effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
