The aim of this conference is to bring
together the diverse collection to
discuss current trends and future
directions in this area.
In graph theory, the spectra of matrices
associated with a graph are widely used
to characterize its properties and to
extract structural information. There
are several graph matrix representations
such as the adjacency matrix,
combinatorial Laplacian, normalized
Laplacian and signless Laplacian.
Spectral graph theory has also many
applications in other scientific fields
such as chemistry, theoretical physics,
and quantum mechanics. The aim of this
workshop is to foster the connections
between spectral graph theory and
computer science.
