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Network clustering (or graph partitioning) is an important task for the discovery of underlying structures in networks. Many algorithms find clusters by maximizing the number of intra-cluster edges. While such algorithms find useful and interesting structures, they tend to fail to identify and isolate two kinds of vertices that play special roles - vertices(More)
BACKGROUND Biological systems can be modeled as complex network systems with many interactions between the components. These interactions give rise to the function and behavior of that system. For example, the protein-protein interaction network is the physical basis of multiple cellular functions. One goal of emerging systems biology is to analyze very(More)
Graph partitioning, or network clustering, is an essential research problem in many areas. Current approaches, however, have difficulty splitting two clusters that are densely connected by one or more " hub " vertices. Further, traditional methods are less able to deal with very confused structures. In this paper we propose a novel similarity-based(More)
In this paper, we describe a new approach for mining concept associations from large text collections. The concepts are short sequences of words that occur frequently together across the text collections. It is these concepts that convey most of the meaning in any language. Our goal is to extract interesting associations among concepts that co-occur within(More)
Many systems in sciences, engineering and nature can be modeled as networks. Examples are internet, metabolic networks and social networks. Network clustering algorithms aimed to find hidden structures from networks are important to make sense of complex networked data. In this paper we present a new clustering method for networks. The proposed algorithm(More)
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