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Google Buzz is a novel online service that presents new opportunities for social network analysis. By initializing the Buzz network with existing Gmail contacts, Google provides a unique dataset that may reflect a different aspect of online communication from those found in existing networks such as Facebook and Twitter. In this paper we design heuristic metrics for ranking and recommending influential members of the Buzz social network. We leverage these metrics to develop an application allowing individual Buzz users to identify influential users near their existing “friend” subgraph.