Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering
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Abstract (arXiv)
The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a novel density-based unsupervised learning method that determines cluster centers by constructing a potential function. In this method, we use the Graph Gradient Descent algorithm to find the centers of clusters. GPU parallelization is utilized for computing potential values. We also conducted experiments on five widely used datasets and evaluated using four indicators. The results show superior performance of the method. Finally, we discuss the influence of σ on the experimental results.
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References · 4
- 2017Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rankingcited by 332
- 2017Cooperative Game Theory Approaches for Network Partitioningcited by 134
- 2019Attributed Graph Clustering via Adaptive Graph Convolutioncited by 96
- 2017Hierarchical Clustering: Objective Functions and Algorithmscited by 60
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