A new multi-level algorithm for balanced partition problem on large scale directed graphs

Graph partition is a classical combinatorial optimization and graph theory problem, and it has a lot of applications, such as scientific computing, VLSI design and clustering etc. In this paper, we study the partition problem on large scale directed graphs under a new objective function, a new instance of graph partition problem. We firstly propose the modeling of this problem, then design an algorithm based on multi-level strategy and recursive partition method, and finally do a lot of simulation experiments. The experimental results verify the stability of our algorithm and show that our algorithm has the same good performance as METIS. In addition, our algorithm is better than METIS on unbalanced ratio.

A New Multi-level Algorithms for Balanced Partition Problem on Large Scale Directed Graphs

Graph partition is a classical combinatorial optimization and graph theory problem, and it has a lot of applications, such as scientific computing, VLSI design and clustering etc. In this paper, we study the partition problem on large scale directed graphs under a new objective function, a new case of graph partition problem. We firstly propose the modeling of this problem, then design an algorithm based on multi-level strategy and recursive partition method, and finally do a lot of simulation experiments. The experimental results verify the stability of our algorithm and show that our algorithm has the same good performance as METIS. In addition, our algorithm is better than METIS on unbalanced ratio.

The Spectral Clustering Based on the Most Similar Relation Diagram

In this paper we present a spectral clustering method based on the MSRD (Most Similar Relation Diagram). The feature of this method is that both the constructing of the adjacency matrix and the clustering are achieved by spectral algorithm. Experiment on an artificial datasets demonstrate that our method can generate balanced partition and detect the manifold clusters no matter the unnormalized or normalized Laplassian is used and can generate partitions with different features if different MSRD is used. Experiments on some real datasets proved that our method is valid and effective.