We discuss new distributed message-passing-based graph mining frameworks based on MapReduce, Pregel, and ASYMP. Our goal is to develop graph mining algorithms for trillions of nodes. Considering both synchronous and asynchronous message-passing frameworks, we discuss algorithmic challenges and ideas from local graph clustering, and map-reduce-based algorithms, and design scalable graph clustering algorithms.

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