Many of today's applications can benefit from the discovery of the most central entities in real-world networks. This paper presents a new technique that efficiently finds the k most central entities in terms of closeness centrality. Instead of computing the centrality of each entity independently, our technique shares intermediate results between centrality computations. Since the cost of each centrality computation may vary substantially depending on the choice of the previous computation, our technique schedules centrality computations in a manner that minimizes the estimated completion time. This technique also updates, with negligible overhead, an upper bound on the centrality of every entity. Using this information, our technique proactively skips entities that cannot belong to the final answer. This paper presents evaluation results for actual networks to demonstrate the benefits of our technique.

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This paper describes initiatives at Marist College to develop a Game Concentration in the undergraduate Computer Science curriculum. These initiatives contemplate recommendations for existing courses as well as adoption of new courses. We also consider activities of the Association of Computing Machinery (ACM) in this area and opportunities for students beyond the classroom.