regardless of the sample selection. 2001 – The completed two-degree Field Galaxy Redshift Survey adds a significantly large amount of voids to the database of all known cosmic voids. 2009 – The Sloan Digital Sky Survey (SDSS) data combined with previous large-scale surveys now provide the most complete view of the detailed structure of cosmic voids. Methods for finding There exist a number of ways for finding voids with the results of large-scale surveys of the universe. Of the many different algorithms, virtually all fall into one of three general categories. The first class consists of void finders that try to find empty regions of space based on local galaxy density. The second class are those which try to find voids via the geometrical structures in the dark matter distribution as suggested by the galaxies. The third class is made up of those finders which identify structures dynamically by using gravitationally unstable points in the distribution of dark matter. The three most popular methods through the study of cosmic voids are listed below: VoidFinder algorithm This first-class method uses each galaxy in a catalog as its target and then uses the Nearest Neighbor Approximation to calculate the cosmic density in the region contained in a spherical radius determined by the distance to the third-closest galaxy. El Ad & Piran introduced this method in 1997 to allow a quick and effective method for standardizing the cataloging of voids. Once the spherical cells are mined from all of the structure data, each cell is expanded until the underdensity returns to average expected wall density values. One of the helpful features of void regions is that their boundaries are very distinct and defined, with a cosmic mean density that starts at 10% in the body and quickly rises to 20% at the edge and then to 100% in the walls directly outside the edges. The remaining walls and overlapping void regions are then gridded into, respectively, distinct and intertwining zones of filaments, clusters, and near-empty voids. Any overlap of more than 10% with already known voids are considered to be subregions within those known voids. All voids admitted to the catalog had a minimum radius of 10 Mpc in order to ensure all identified voids were not accidentally cataloged due to sampling errors. Zone bordering on voidness (ZOBOV) algorithm This particular second-class algorithm uses a Voronoi tessellation technique and mock border particles in