Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Enumeration of the Elementary Circuits of a Directed Graph
Enumeration of the Elementary Circuits of a Directed Graph
Small Worlds: The Dynamics of Networks between Order and Randomness
Small Worlds: The Dynamics of Networks between Order and Randomness
A new way to enumerate cycles in graph
AICT-ICIW '06 Proceedings of the Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services
Analysis of temporal evolution of social networks
Journal of Mobile Multimedia
Investigating the Properties of a Social Bookmarking and Tagging Network
International Journal of Data Warehousing and Mining
The Dynamics of Content Popularity in Social Media
International Journal of Data Warehousing and Mining
Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor
International Journal of Data Warehousing and Mining
Special issue on Semantic Information Management guest editorial
Information Systems Frontiers
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Complex networks can store information in form of periodic orbits (cycles) existing in the network. This cycle-based approach although computationally intensive, it provided us with useful information about the behavior and connectivity of the network. Social networks in most works are treated like any complex network with minimal sociological features modeled. Hence the cycle distribution will suggest the true capacity of this social network to store information. Counting cycles in complex networks is an NP-hard problem. This work proposed an efficient algorithm based on statistical mechanical based Belief Propagation (BP) algorithm to compute cycles in different complex networks using a phenomenological Gaussian distribution of cycles. The enhanced BP algorithm was applied and tested on different networks and the results showed that our model accurately approximated the cycles distribution of those networks, and that the best accuracy was obtained for the random network. In addition, a clear improvement was achieved in the cycles computation time. In some cases the execution time was reduced by up to 88 % compared to the original BP algorithm.