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The study of meme propagation and the prediction of meme trajectory are emerging areas of interest in the field of complex networks research. In addition to the properties of the meme itself, the structural properties of the underlying network decides the speed and the trajectory of the propagating meme. In this paper, we provide an artificial framework for studying the meme propagation patterns. Firstly, the framework includes a synthetic network which simulates a real world network and acts as a testbed for meme simulation. Secondly, we propose a meme spreading model based on the diversity of edges in the network. Through the experiments conducted, we show that the generated synthetic network combined with the proposed spreading model is able to simulate a real world meme spread. Our proposed model is validated by the propagation of the Higgs boson meme on Twitter as well as many real world social networks.

Comprehending the virality of a meme can help scientists address problems pertaining to disciplines like epidemiology and digital marketing. Therefore, it does not come as a surprise that meme virality stands out as an integral component of research in complex networks, today. In this paper, we explore the possibility of artificially inducing virality in a meme by intelligently directing a meme's trajectory in the network. Keeping in mind the importance of core nodes in a core-periphery structure, we propose two shell-based hill climbing algorithms to determine the path from a node in the periphery shell (where the memes generally originate) to the core of the network. On performing further simulations and analysis on the networks behavioral characteristics, we were also able to unearth specialized shells which we termed Pseudo-Cores. These shells emulate the behavior of the core in terms of the size of the cascade triggered. In our experiments, we have considered two sets for the target nodes, one being core and the other being any of the pseudo-cores. We compare our algorithms against already existing path finding algorithms and validate the better performance of our algorithms experimentally.

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It has been argued that the reservation system in India, which has existed since the time of Indian Independence (1947), has caused more havoc and degradation than progress. This being a popular public opinion, has not been based on any rigorous scientific study or research. In this paper, we revisit the cultural divide among the Indian population from a purely social network based approach. We study the distinct cluster formation that takes place in the Indian community and find that this is largely due to the effect of caste-based homophily. To study the impact of the reservation system, we define a new parameter called social distance that represents the social capital associated with each individual in the backward class. We study the changes that take place with regard to the average social distance of a cluster when a new link is established between the clusters which in its essence, is what the reservation system is accomplishing. Our extensive study calls for the change in the mindset of people in India. Although the animosity towards the reservation system could be rooted due to historical influence, hero worship and herd mentality, our results make it clear that the system has had a considerable impact on the country's overall development by bridging the gap between the conflicting social groups. The results also have been verified using the survey and are discussed in the paper.