Limited Random Walk Algorithm for Big Graph Data Clustering

TL;DR

This paper introduces a novel limited random walk algorithm for big graph data clustering that effectively finds clusters using attracting vertices, outperforming existing methods and suitable for parallel computing environments.

Contribution

The paper presents a new random-walk-based clustering algorithm that restricts walk reach and uses attracting vertices, offering improved performance on large-scale graphs.

Findings

Superior clustering performance on real-world big graphs.

Efficient parallel implementation suitable for large datasets.

Capable of clustering graphs with millions of vertices.

Abstract

Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackles the problem in an entirely different manner. We use the limited random walk procedure to find attracting vertices in a graph and use them as features to cluster the vertices. According to the experimental results on the simulated graph data and the real-world big graph data, the proposed method is superior to the state-of-the-art methods in solving graph clustering problems. Since the proposed method uses the embarrassingly parallel paradigm, it can be efficiently implemented and embedded in any parallel computing environment such as a MapReduce framework. Given enough computing resources, we are capable of clustering graphs with millions of vertices and hundreds millions of edges in a reasonable time.