A Novel Social Network Model for Research Collaboration

Recent studies on social networks are based on a characteristic which includes assortative mixing, high clustering, short average path lengths, broad degree distributions and the existence of community structure. Here, an application has been developed in the domain of ‘research collaboration’ which satisfies all the above characteristics, based on some existing social network models. In addition, this application facilitates interaction between various communities (research groups). This application gives very high clustering coefficient by retaining the asymptotically scale-free degree distribution. Here the community structure is raised from a mixture of random attachment and implicit preferential attachment. In addition to earlier works which only considered Neighbor of Initial Contact (NIC) as implicit preferential contact, we have considered Neighbor of Neighbor of Initial Contact (NNIC) also. This application supports the occurrence of a contact between two Initial contacts if the new vertex chooses more than one initial contacts. This ultimately will develop a complex research social network rather than the one that was taken as basic reference.