Extraction of academic social network from online database

Abstract

There has been quite a number of research efforts in extracting academic social network from on-line open sources such as the DBLP, ACM DL and IEEXplore. Extraction of such a network is usually based on the concept of co-occurrences. One of the issues in such efforts is actually involved extracting reliable and trusted network particularly when dealing with the heterogeneity of features in the Web. In this paper we demonstrate the use of association rule to enhance existing superficial method for extracting social network from online database such as the DBLP. The approach proposed has shown the capacity to extract social relation as well as the strength of these relations.

Social Network Mining (SNM): A Definition of Relation between the Resources and SNA

“…Social Network Mining (SNM) have been declared as social network data mining [11] is to refer to the definition of the data mining: a process for discovering useful information automatically from the Web such as the large data repositories. The SNM techniques are deployed to explore large structure in order to formulate rules that can adopt useful patterns where they may have not unknown [7]. To obtain the social structures from the unstructured information, where one of them is a social network, is by extracting the information (social network) from the Web [12].…”

“…2, we conduct some computations as follows, where the degree of vertices as the basis of SNA [25]. First, we collect a set of actors as seeds (sample) for generating social networks [7], and we conduct a random test of sample against sorted names. Seeds and papers are data obtained during the pre-processing of the SNM.…”

“…In this case, an online database according to its size can refer to as a big data. Therefore, the proposed method is based on the principle of extraction can be used for evaluating the big data [7].…”

“…In this case, the computer network as the social network [3] implies that the Web as social media containing the big data [4] to be a big picture of the real world, and in the structure we can express it through the social network [5], [6]. Currently, one of the results of social network is a resultant from the methods of social network extraction, whereby actors/vertices, relationship/edges, and web/documents are resources of social network [6], [7]. Social network as information about the behaviour of social actors to be important in decision making such as the importance of information source like Web.…”

“…This led to the extraction of social networks be more complex and involves data increasingly large, while extracting the social network depends heavily on the limited services of tools such as search engine [12]. Therefore, extracting the social networks from information sources like the Web relatively just based on samples [7], and it is useful to learn the behaviour of the social network. That is, Social Network Mining (SNM) to provide a means of discovering the behaviour of either the information sources or the resource, SNM is not same as the SNA, but to overcome a bit of information connectedness between them.…”