Social Networks Analysis: Basics, Measures and Visualizing Authorship Networks in DBLP Data

Citation:
Panda, M., N. El-Bendary, M. Salama, A. E. Hassanien, and A. Abraham, "Social Networks Analysis: Basics, Measures and Visualizing Authorship Networks in DBLP Data", Computational Social Networks: Mining and Visualization, London, Series in Computer Communications and Networks, Springer Verlag, 2012.

Abstract:

Social Network Analysis (SNA) is becoming an important tool for investigators, but all the necessary information is often available in a distributed environment. Currently there is no information system that helps managers and team leaders to monitor the status of a social network. This chapter presents an overview of the basic concepts of social networks in data analysis including social networks analysis metrics and performances. Different problems in social networks are discussed such as uncertainty, missing data and finding the shortest path in social network. Community structure, detection and visualization in social network analysis is also discussed and reviewed. This chapter bridges the gap among the users by combining social network analysis methods and information visualization technology to help user visually identify the occurrence of a possible relationship amongst the members in a social network. In addition, briefly describing the different performance measures that have been encountered during any network analysis in order to understand the fundamental behind the comprehension. This chapter also, presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science, which is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. Challenges to be ad dressed and future directions of research are also presented and an extensive bibliography is included.order to understand the fundamental behind the comprehension. This chapter also, presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science, which is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. Challenges to be ad dressed and future directions of research are also presented and an extensive bibliography is included.