BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering

Subjects: Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph)

[…]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. .[…]

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By Honglei Zhang, Jenni Raitoharju, Serkan Kiranyaz, Moncef Gabbouj
Source: arxiv.org