Abstract
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.
Highlights
Networked data are ubiquitous in real-world applications
Heterogeneous information networks (HIN), abstract representations of systems composed of multiple types of entities and their relations, are good candidates to model such data together with
The final model obtained contains five predictors related to meta-paths whose length are no longer than 3 and no intercept. This regression model accounts for 71.29% of the variance
Summary
Networked data are ubiquitous in real-world applications Examples of such data are humans in social activities, proteins in biochemical interactions, pages of Wikipedia or movies-users from Amazon just to name a few [1,2]. In social activities, the links can reflect online or offline communication or more obviously, in the movie-user case, nodes represent two different objects. Taking these differences explicitly into account in the modeling can only enrich the understanding of the inspected system. Heterogeneous information networks (HIN), abstract representations of systems composed of multiple types of entities and their relations, are good candidates to model such data together with
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