  In this paper we explore the use of spatial clustering algorithms as a new
computational approach for modeling the cosmic web. We demonstrate that such
algorithms are efficient in terms of computing time needed. We explore three
distinct spatial methods which we suitably adjust for (i) detecting the
topology of the cosmic web and (ii) categorizing various cosmic structures as
voids, walls, clusters and superclusters based on a variety of topological and
physical criteria such as the physical distance between objects, their masses
and local densities. The methods explored are (1) a new spatial method called
Gravity Lattice ; (2) a modified version of another spatial clustering
algorithm, the ABACUS; and (3) the well known spatial clustering algorithm
HDBSCAN. We utilize HDBSCAN in order to detect cosmic structures and categorize
them using their overdensity. We demonstrate that the ABACUS method can be
combined with the classic DTFE method to obtain similar results in terms of the
achieved accuracy with about an order of magnitude less computation time. To
further solidify our claims, we draw insights from the computer science domain
and compare the quality of the results with and without the application of our
method. Finally, we further extend our experiments and verify their
effectiveness by showing their ability to scale well with different cosmic web
structures that formed at different redshifts.

Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics