Given an edge-labeled graph and two nodes, how can we accurately infer the relation between the nodes? Reasoning how the nodes are related is a fundamental task in analyzing network data, and various relevance measures have been suggested to effectively identify relevance between nodes in graphs. Although many random walk based models have been extensively utilized to reveal relevance between nodes, they cannot distinguish how those nodes are related in terms of edge labels since the traditional surfer does not consider edge labels for estimating relevance scores. In this paper, we propose MuRWR (Multi-Labeled Random Walk with Restart), a novel random walk based model that accurately identifies how nodes are related with, considering multiple edge labels. We introduce a labeled random surfer whose label indicates the relation between starting and visiting nodes, and change the surfer’s label during random walks for multi-hop relational reasoning. We also learn appropriate rules on changing the surfer’s label from the edge-labeled graph to accurately infer relations. We develop an iterative algorithm for computing MuRWR, and prove the convergence guarantee of the algorithm. Through extensive experiments, we show that our model MuRWR provides the best inference performance.

给定一个带有边缘标签的图和两个节点，我们如何准确推断节点之间的关系？推理节点之间的关系是分析网络数据的基本任务，并且已经提出了各种相关性措施以有效地识别图中节点之间的相关性。尽管已广泛利用许多基于随机游动的模型来揭示节点之间的相关性，但是由于传统的冲浪者不考虑使用边缘标签来估计相关性得分，因此它们无法区分这些节点在边缘标签方面如何相关。在本文中，我们提出了具有重新启动功能的多标签随机游动（MuRWR）），这是一种新颖的基于随机游动的模型，可以考虑多个边缘标签来准确识别节点之间的关系。我们引入了一个带标签的随机冲浪者，其标签指示了起始节点和访问节点之间的关系，并在随机游走期间更改了冲浪者的标签，以进行多跳关系推理。我们还从边缘标记的图形中了解了更改冲浪者标签以正确推断关系的适当规则。我们开发了一种用于计算MuRWR的迭代算法，并证明了该算法的收敛性。通过广泛的实验，我们证明了我们的模型MuRWR提供了最佳的推理性能。