Combining Path-Constrained Random Walks to Recover Link Weights in Heterogeneous Information Networks
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Heterogeneous information networks (HINs) are 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 HINs. Actually, this weight is approximated by a linear combination of probabilities, results of 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 which is commonly called a meta path, performed on the HINs. This method is general enough to compute the link weight between any types of nodes. Experiments on Twitter data show the applicability of the method.
This work is funded in part by the European Commission H2020 FETPROACT 2016-2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grant ANR-15- E38-0001 (AlgoDiv), and by the Île-de-France Region and its program FUI21 under grant 16010629 (iTRAC).
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