The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group. Community mining or detection involves discovering groups in a network where an individual's group memberships are not explicitly given. Much of the research on detecting communities in co-authorship networks has been done using publicly available datasets....
With the development of SNS(social network service), social network has become more and more important. The problem of how to discover users' (including the Internet and the telecommunication network) social network from users' interaction detail between each other starts to be the focus problem of researchers. Basing on the analysis of telecommunication call data, this paper studied how CDRs(Call...
In this paper, we focus on a single graph whose vertices contain a set of quantitative attributes. Several networks can be naturally represented in this complex graph. An example is a social network whose vertex corresponds to a person with some quantitative items such as age, salary and so on. Although it can be expected that this kind of data will increase rapidly, most of current graph mining algorithms...
Many systems in sciences, engineering and nature can be modeled as networks. Examples include the Internet, WWW and social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted...
Communities in social networks may overlap, with some hub nodes belonging to multiple communities. They may also have outliers, which are nodes that belong to no community. The criterion to locate hubs or outliers is network dependent. Previous methods usually require this information as input parameters, e.g., an expected number of communities, with no intuition or assistance. Here we present a visual...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.