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Although many different community detection algorithms have been proposed to detect community structures in complex networks, how to effectively detect community structures is still a great challenge. Seed-centric methods is one of the most effective solutions for community detection. To more, in this paper, we propose a novel density-based seed expansion algorithm, namely, DenSeC, which can easily...
Utilizing graph analysis models and algorithms to exploit complex interactions over a network of entities is emerging as an attractive network analytic technology. In this paper, we show that traditional column or row-based trace analysis may not be effective in deriving deep insights hidden in the storage traces collected over complex storage applications, such as complex spatial and temporal patterns,...
Many popular web service networks are content-rich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have...
Clustering is an important technology in data mining. Squeezer is one such clustering algorithm for categorical data and it is more efficient than most existing algorithms for categorical data. But Squeezer is time consuming for very large datasets which are distributed in different servers. Thus, we employ the distributed thinking to improve Squeezer and a distributed algorithm for categorical data...
Service network analysis is an essential aspect of web service discovery, search, mining and recommendation. Many popular web service networks are content-rich in terms of heterogeneous types of entities, attributes and links. A main challenge for ranking services is how to incorporate multiple complex and heterogeneous factors, such as service attributes, relationships between services, relationships...
In recent years, many networks have become available for analysis, including social networks, sensor networks, biological networks, etc. Graph clustering has shown its effectiveness in analyzing and visualizing large networks. The goal of graph clustering is to partition vertices in a large graph into clusters based on various criteria such as vertex connectivity or neighborhood similarity. Many existing...
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