MATRIX RESOLVENT EIGENEMBEDDINGS FOR DYNAMIC GRAPHS
Vasileios Kalantzis (IBM Research); Panagiotis Traganitis (Michigan State University)
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06 Jun 2023
Eigenvector embeddings have been widely used to study
graph properties in signal processing, mining, and learning
tasks. However, if a graph is changing dynamically, these embeddings have to be recomputed. In this work we introduce
a novel matrix resolvent expansion-based projection scheme
to update eigenvector embeddings of dynamic graphs. The
proposed method can tackle graph updates where both new
vertices and edges are added, and its potential is illustrated
via numerical tests on real data.
