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FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks.

Jing Yang¹, Limin Chen², Jianpei Zhang¹

¹Institute of Computer Science and Technology, Harbin Engineering University, Harbin, China.

|June 20, 2015

Summary

This summary is machine-generated.

A new fast clustering algorithm, FctClus, efficiently clusters heterogeneous information networks. It uses approximate commute time embedding for improved accuracy and speed in star network schemas.

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Area of Science:

Computer Science
Data Mining
Network Analysis

Background:

Clustering heterogeneous information networks is crucial for data analysis.
Existing methods may lack efficiency or generalizability for complex network structures.

Purpose of the Study:

To propose a fast and accurate clustering algorithm for heterogeneous information networks with a star schema.
To leverage network sparsity for computational efficiency.

Main Methods:

Transforming heterogeneous networks into compatible bipartite graphs.
Computing approximate commute time embeddings using random mapping and linear solvers.
Developing a general model and fast algorithm (FctClus) for simultaneous clustering.

Main Results:

FctClus demonstrates high clustering accuracy.
The algorithm achieves fast computation speeds.
Theoretical analysis and experimental verification support the findings.

Conclusions:

FctClus is an efficient and generalizable algorithm for heterogeneous information network clustering.
The approach effectively utilizes network sparsity for performance gains.