We have considered a simple word game called the word-morph. After making our participants play a stipulated number of word-morph games, we have analyzed the experimental data. We have given a detailed analysis of the learning involved in solving this word game. We propose that people are inclined to learn landmarks when they are asked to navigate from a source to a destination. We note that these landmarks are nodes that have high closeness-centrality ranking.

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Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. Weighted centrality measures usually consider weights on the edges and assume the weights on the nodes to be uniform. One of the main reasons for this assumption is the hardness and challenges in mapping the nodes to their corresponding weights. In this paper, we propose a way to overcome this kind of limitation by hybridization of the traditional centrality measures. The hybridization is done by taking one of the centrality measures as a mapping function to generate weights on the nodes and then using the node weights in other centrality measures for better complex ranking.

Complex networks have gained more attention from the last few years. The size of the real world complex networks, such as online social networks, WWW networks, collaboration networks, is exponentially increasing with time. It is not feasible to completely collect, store and process these networks. In the present work, we propose a method to estimate the degree centrality ranking of a node without having complete structure of the graph. The proposed algorithm uses degree of a node and power law exponent of the degree distribution to calculate the ranking. We also study simulation results on Barabasi-Albert model. Simulation results show that the average error in the estimated ranking is approximately 5% of the total number of nodes.