Nowadays graphs are widely used in many domains such as software, network, web, chemistry, biology and even communication and sociology to modelling and data processing. In many applications, graphs are very large and complex. So understanding the structure and extracting useful information from them is become more challenging. Here, graph summarization algorithms could be a suitable solution. In this paper, a new graph summarization algorithm has been proposed which is able to produce different summaries from different points of view from one graph regarding to user’s interested subjects. Also users can control the resolution of produced summaries. Moreover, the algorithm is developed using Neo4j database which is one of NoSQL databases. Also, the algorithm using different laboratorial and real data sets is tested. The results show that the produced summaries are in high quality position and also the efficiency and scalability of the algorithm is better that the similar one.
Arsanjani, M. M., & Kangavari, M. R. (2012). Introducing a Novel Algorithm SISG to Semantically Summarize Massive Graphs. Journal of Advanced Defense Science & Technology, 3(2), 145-158.
MLA
Mohammad Mahdi Arsanjani; Mohammad Reza Kangavari. "Introducing a Novel Algorithm SISG to Semantically Summarize Massive Graphs". Journal of Advanced Defense Science & Technology, 3, 2, 2012, 145-158.
HARVARD
Arsanjani, M. M., Kangavari, M. R. (2012). 'Introducing a Novel Algorithm SISG to Semantically Summarize Massive Graphs', Journal of Advanced Defense Science & Technology, 3(2), pp. 145-158.
VANCOUVER
Arsanjani, M. M., Kangavari, M. R. Introducing a Novel Algorithm SISG to Semantically Summarize Massive Graphs. Journal of Advanced Defense Science & Technology, 2012; 3(2): 145-158.
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