# Link weights recovery in heterogeneous information networks

## TL;DR

This paper presents a method to recover missing link weights in heterogeneous information networks by combining path-constrained random walk probabilities, enabling better understanding of complex socio-technical systems.

## Contribution

It introduces a general approach to estimate link weights in HINs using meta-path-based random walks, applicable across various node types and networks.

## Key findings

- Effective recovery of link weights demonstrated on Twitter data.
- Method accurately estimates unobserved link weights.
- Applicable to diverse heterogeneous networks.

## Abstract

Socio-technical systems usually consists 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 in order 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.