**Dianbin Bao**

University of Notre Dame

**3:30 PM**

**154 Hurley Hall**

**'Network Distance Based on Laplacian Flow on Graphs**"

**Distance between objects plays an important role in spectral clustering and machine learning. In this talk, we focus on proposing a distance between network objects. We first introduce a dynamic system on graphs called Laplacian flow. Based on this Laplacian flow, a new version of diffusion distance between networks is proposed. We will demonstrate the utility of the distance and its advantage over various existing distances such as Hamming distance through explicit examples. The distance is also applied for learning tasks such as for clustering network objects.**

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