43 citations · 80 across the 25 of their papers we have counts for
5 papers · 2 filters
Higher-order organization of complex networks
Austin R. Benson, David F. Gleich, Jure Leskovec
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibi…
Mining and modeling character networks
Anthony Bonato, David Ryan D'Angelo, Ethan R. Elenberg +2
We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as s…
An optimization approach to locally-biased graph algorithms
Kimon Fountoulakis, David Gleich, Michael Mahoney
Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a large data graph. In some cases, this can be accomplished by adding some sor…
A Simple and Strongly-Local Flow-Based Method for Cut Improvement
Nate Veldt, David F. Gleich, Michael W. Mahoney
Many graph-based learning problems can be cast as finding a good set of vertices nearby a seed set, and a powerful methodology for these problems is based on maximum flows. We intr…
General Tensor Spectral Co-clustering for Higher-Order Data
Tao Wu, Austin R. Benson, David F. Gleich
Spectral clustering and co-clustering are well-known techniques in data analysis, and recent work has extended spectral clustering to square, symmetric tensors and hypermatrices de…