A knowledge structures exploration on social network sites

Purpose – This paper aims to describe a method for combining perceived community support, relationship quality and the extended technology acceptance model in the same empirically derived associative network. The research also examines the moderating role of accumulation of knowledge (based on beliefs and opinions) derived from social interactions. Design/methodology/approach – The Path fi nder algorithm is a valid approach for determining network structures from relatedness data. Such a graphical representation provides managers with a comprehensible picture of how social behaviours relate to loyalty-based dimensions. Findings – As the bene fi ts of community participation and integration might be differently evaluated by new and long-term users, the research examines the associative network by levels of user familiarity. This study indeed contributes to the analysis of enduring social bonds with respect to individuals ’ decision-making processes, as it provides details representing speci fi c relationships between diverse concepts based on true- loyalty. Practical implications – The application of Path fi nder to the study of online social services and user behaviour appears to have potential for unveiling the structures of social network sites members and designing successful strategies for prospective community managers. Originality/value – This is the fi rst study to the author ’ s knowledge that empirically tests a theory- grounded framework for integrating individual characteristics and relational driver and focuses on associative structures evidenced as a representation of the most salient loyalty-based concepts by also studying the moderating effects of familiarity.Junta de Andalucía SEJ-580

Construction of near-optimal vertex clique covering for real-world networks

We propose a method based on combining a constructive and a bounding heuristic to solve the vertex clique covering problem (CCP), where the aim is to partition the vertices of a graph into the smallest number of classes, which induce cliques. Searching for the solution to CCP is highly motivated by analysis of social and other real-world networks, applications in graph mining, as well as by the fact that CCP is one of the classical NP-hard problems. Combining the construction and the bounding heuristic helped us not only to find high-quality clique coverings but also to determine that in the domain of real-world networks, many of the obtained solutions are optimal, while the rest of them are near-optimal. In addition, the method has a polynomial time complexity and shows much promise for its practical use. Experimental results are presented for a fairly representative benchmark of real-world data. Our test graphs include extracts of web-based social networks, including some very large ones, several well-known graphs from network science, as well as coappearance networks of literary works' characters from the DIMACS graph coloring benchmark. We also present results for synthetic pseudorandom graphs structured according to the Erdös-Renyi model and Leighton's model