Abstract
Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them. One of the new approaches to describe knowledge of the social network is through a concise structure called conceptual view. In order to build this view, it is first needed to extract conceptual links from the intended network. However, extracting these links for large scale networks is very time consuming. In this paper, a new algorithm for extracting frequent conceptual link from social networks is provided where by introducing the concept of dependency, it is tried to accelerate the process of extracting conceptual links. Although the proposed algorithm will be able to accelerate this process if there are dependencies between data, but the tests carried out on Pokec social network, which lacks dependency between its data, revealed that absence of dependency, increases execution time of extracting conceptual links only up to 15 percent.
Highlights
INTRODUCTIONSocial network is a social structure that is composed of some agents (generally individuals or organizations) that are connected by one or more kind of dependencies, such as ideas and financial transactions, friends, relatives, web links, spread of diseases (epidemiology)
Social network is a social structure that is composed of some agents that are connected by one or more kind of dependencies, such as ideas and financial transactions, friends, relatives, web links, spread of diseases
D-MFCLMin algorithm is presented which using the concept of dependence, and by pruning the search space, tries to reduce the time required to extract frequent conceptual links
Summary
Social network is a social structure that is composed of some agents (generally individuals or organizations) that are connected by one or more kind of dependencies, such as ideas and financial transactions, friends, relatives, web links, spread of diseases (epidemiology). Primitive methods in this field have been using measures deriving from graph theory [2], new approaches known as social networks mining or link mining try to examine features of node in addition to the network structure to extract a new set of patterns [3]-[5]. Conceptual link provides the knowledge about groups of nodes that densely connected to each other in a social network, and through a reduced structure, which is called as conceptual view, leads to a semantic view of social network. D-MFCLMin algorithm is presented which using the concept of dependence, and by pruning the search space, tries to reduce the time required to extract frequent conceptual links.
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