Affiliations: NIT, Zhejiang University, Ningbo, Zhejiang Province, China. [email protected] | Hong Kong Baptist University, Kowloon Tong, Hong Kong. [email protected] | Center for Applied Informatics, Victoria University, Australia. [email protected]
Note: [] Address for correspondence: NIT, Zhejiang University, Ningbo, Zhejiang Province, China
Abstract: Online social networks (OSN) are facing challenges since they have been extensively applied to different domains including online social media, e-commerce, biological complex networks, financial analysis, and so on. One of the crucial challenges for OSN lies in information overload and network congestion. The demands for efficient knowledge discovery and data mining methods in OSN have been rising in recent year, particularly for online social applications, such as Flickr, YouTube, Facebook, and LinkedIn. In this paper, a Belief-Desire-Intention (BDI) agent-based method has been developed to enhance the capability of mining online social networks. Current data mining techniques encounter difficulties of dealing with knowledge interpretation based on complex data sources. The proposed agent-based mining method overcomes network analysis difficulties, while enhancing the knowledge discovery capability through its autonomy and collective intelligence.
Keywords: Online social networks, agent networks, AOC, BDI agents
