Abstract
Abstract The analysis of social networks has received much attention in recent years. Most social structures are represented as unipartite graphs or bipartite affiliation networks. However, more complex topologies are becoming popular within social networking community. An example of such structure is a Folksonomy: a tuple of connections among users, resources and tags. An intuitive way to represent a Folksonomy is a three-mode hypergraph. It has been shown that in such graphs a clustering coefficient decreases slowly over time at very high level and this property is unachievable for simple random hypergraphs. In this article we represent a Folksonomy as a tripartite graph. This small change of perspective enables us to divide graph generation process into two steps and adapt algorithms used for bipartite graph generation at each step. As a result we obtain iteratively graphs that reflect both dynamics and high level of clustering coefficient.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
