Socio-technical systems usually consist of many intertwined networks, each connecting different types of objects or actors through a variety of means. As these networks are co-dependent, one can take advantage of this entangled structure to study interaction patterns in a particular network from the information provided by other related networks. A method is, hence, proposed and tested to recover the weights of missing or unobserved links in heterogeneous information networks (HIN)—abstract representations of systems composed of multiple types of entities and their relations. Given a pair of nodes in a HIN, this work aims at recovering the exact weight of the incident link to these two nodes, knowing some other links present in the HIN. To do so, probability distributions resulting from path-constrained random walks, i.e., random walks where the walker is forced to follow only a specific sequence of node types and edge types, capable to capture specific semantics and commonly called a meta-path, are combined in a linearly fashion to approximate the desired result. This method is general enough to compute the link weight between any types of nodes. Experiments on Twitter and bibliographic data show the applicability of the method.

中文翻译：

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异构信息网络中的链路权重恢复

社会技术系统通常由许多相互交织的网络组成，每个网络都通过各种方式连接不同类型的对象或参与者。由于这些网络是相互依赖的，因此可以利用这种纠缠的结构从其他相关网络提供的信息中研究特定网络中的交互模式。因此，提出并测试了一种方法来恢复异构信息网络（HIN）中丢失或未观察到的链接的权重-由多种类型的实体及其关系组成的系统的抽象表示。给定一个HIN中的一对节点，这项工作的目的是在知道HIN中存在其他一些链接的情况下，恢复到这两个节点的事件链接的精确权重。为此，由路径受限的随机游走产生的概率分布，即 随机游走被强制以仅遵循特定类型的节点类型和边缘类型的顺序（能够捕获特定的语义并通常称为元路径）的方式进行随机组合，以线性方式进行组合，以近似所需的结果。此方法足够通用，可以计算任何类型的节点之间的链路权重。Twitter和书目数据上的实验表明了该方法的适用性。