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There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers.(More)
Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common property of these networks is their community structure, considered as clusters of densely connected groups of vertices, with only sparser connections between groups. The(More)
Extracting information from very large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities in the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can(More)
Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected(More)
Web site structures are complex to analyze. Cross-referencing the web structure with navigational behaviour adds to the complexity of the analysis. However, this convoluted analysis is necessary to discover useful patterns and understand the navigational behaviour of web site visitors, whether to improve web site structures, provide intelligent on-line(More)
Effectively organizing web search results into clusters is important to facilitate quick user navigation to relevant documents. Previous methods may rely on a training process and do not provide a measure for whether page clustering is actually required. In this paper, we reformalize the clustering problem as a word sense discovery problem. Given a query(More)
As the volume of digitally accessible information grows, there is increasing pressure on the development of data visualization methods to enable humans to interpret that data. We provide a description of our WebViz system, as a tool to visualize both the structure and usage of web sites. We illustrate the use of our visualization paradigm by introducing(More)
—Meerkat is a tool for visualization and community mining of social networks. It is being developed to offer novel algorithms and functionality that other tools do not possess. Meerkat's features include navigation through graphical representations of networks, network querying and filtering, a multitude of graphical layout algorithms, community mining(More)
Finding communities is an important task for the discovery of underlying structures in social networks. While existing approaches give interesting results, they typically neglect the fact that communities may overlap, with some hub nodes participating in multiple communities. Similarly, most methods cannot deal with outliers, which are nodes that belong to(More)
A significant part of the web mining problem is simply in understanding the value of any mining method. For example , the value of web mining to improve user navigation is even more challenging if one can't visualize the differences over a large collection of web pages or a significant structure within the existing web. We present WebKIV, a tool we've(More)