Collection
Data Science on Graphs
There is a fast-growing interest in developing models and tools for analyzing data and capturing the intricate interactions in complex systems such as biological, technological, and social networks. However, the data associated with these systems is typically high-dimensional and irregular, posing novel challenges to classical data science methodologies. To cope with these challenges, the fields of graph signal processing and geometric deep learning have respectively extended classical signal processing tools and machine learning principles to data defined on graphs. An alternative set of tools to handle complex data has been developed within the field of topological data analysis, which concerns itself with the modeling of data sets by geometric objects such as graphs or simplicial complexes.
Editors
-
Santiago Segarra (Guest Editor)
W. M. Rice Trustee Assistant Professor, Rice University, USA
-
Gunnar Carlsson (APCT Editor)
Professor, Stanford University, USA
Articles (10 in this collection)
-
-
-
Brain chains as topological signatures for Alzheimer’s disease
Authors (first, second and last of 5)
-
-
Hypergraph co-optimal transport: metric and categorical properties
Authors (first, second and last of 5)
-
GIST: distributed training for large-scale graph convolutional networks
Authors (first, second and last of 8)
-
-
-
Heat diffusion distance processes: a statistically founded method to analyze graph data sets
Authors
-
