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Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in realtime. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this talk we present a framework for studying graph pattern mining on timevarying streams and large datasets.
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