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PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks

机译:pRep:基于概率视角的基于路径的相关性   异构信息网络

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摘要

As a powerful representation paradigm for networked and multi-typed data, theheterogeneous information network (HIN) is ubiquitous. Meanwhile, definingproper relevance measures has always been a fundamental problem and of greatpragmatic importance for network mining tasks. Inspired by our probabilisticinterpretation of existing path-based relevance measures, we propose to studyHIN relevance from a probabilistic perspective. We also identify, fromreal-world data, and propose to model cross-meta-path synergy, which is acharacteristic important for defining path-based HIN relevance and has not beenmodeled by existing methods. A generative model is established to derive anovel path-based relevance measure, which is data-driven and tailored for eachHIN. We develop an inference algorithm to find the maximum a posteriori (MAP)estimate of the model parameters, which entails non-trivial tricks. Experimentson two real-world datasets demonstrate the effectiveness of the proposed modeland relevance measure.
机译:作为网络化和多类型数据的强大表示范例,异构信息网络(HIN)随处可见。同时,定义适当的相关性措施一直是一个基本问题,对于网络挖掘任务具有非常重要的现实意义。受我们对现有基于路径的相关性措施的概率解释的启发,我们建议从概率的角度研究HIN相关性。我们还从现实世界的数据中进行识别,并建议对跨元路径协同进行建模,这对于定义基于路径的HIN相关性具有重要的特性,并且尚未通过现有方法进行建模。建立生成模型以导出基于anovel路径的相关性度量,该度量是数据驱动的并且针对每个HIN量身定制。我们开发了一个推理算法来找到模型参数的最大后验(MAP)估计,这需要非平凡的技巧。通过两个真实世界的数据集实验,证明了所提模型和相关性度量的有效性。

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