A node clustering algorithm for heterogeneous information networks based on node embeddings

doi:10.1007/s11042-023-15245-9

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A node clustering algorithm for heterogeneous information networks based on node embeddings

2023-05-19

PRE

Dongjiang Liu *

Leixiao Li

DOI：10.1007/s11042-023-15245-9

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Abstract

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Clustering is a very important method to analyze HIN. Thus, several HIN clustering algorithms have been proposed and all these algorithms are based on meta-paths. Meta-path can be used to describe the relationship of target objects. Even though the relationship of target objects are fully considered by these meta-path based algorithms, the directly connected neighbors of target objects are neglected by them. These neglected directly connected neighbors are not target objects, but they contain plenty of useful information for finding clusters of target objects. So, while performing clustering based on HIN, these neglected neighbors should be considered. To achieve the goal, in this paper, a new HIN clustering algorithm is proposed. The proposed algorithm tries to build a vector for each target object. The clustering task is fulfilled based on these vectors. During the vector building process, the neighbors of all the target objects are considered. As clustering result of HIN is affected by different factors, such as neighbors of target objects and different kinds of meta-paths, several similarity matrices are built in the proposed algorithm. Each matrix is corresponding to a specific factor. Besides, every matrix will be assigned a weight value. These weight values are used to represent the relative importance of factors. At the same time, in the proposed algorithm, a new training method is adopted to calculate the vectors and the weight values.

Keywords:

Heterogeneous information networks

Graph mining

Target object

Meta-path

Clustering