# A class of randomized Subset Selection Methods for large complex networks

## TL;DR

This paper introduces randomized subset selection methods to efficiently analyze large complex networks by approximating their spectra and structure, significantly reducing computational time while maintaining accuracy.

## Contribution

It proposes novel randomized heuristics for subset selection in large networks, improving speed and scalability over deterministic methods.

## Key findings

- Randomized methods are faster than deterministic counterparts.
- QR-based randomized subset selection is the quickest approach.
- Methods effectively analyze large, sparse, and dynamic networks.

## Abstract

Most of the real world complex networks such as the Internet, World Wide Web and collaboration networks are huge; and to infer their structure and dynamics one requires handling large connectivity (adjacency) matrices. Also, to find out the spectra of these networks, one needs to perform the EigenValue Decomposition(or Singular Value Decomposition for bipartite networks) of these large adjacency matrices or their Laplacian matrices. In the present work, we proposed randomized versions of the existing heuristics to infer the norm and the spectrum of the adjacency matrices. In an earlier work [1], we used Subset Selection (SS) procedure to obtain the critical network structure which is smaller in size and retains the properties of original networks in terms of its Principal Singular Vector and eigenvalue spectra. We now present a few randomized versions of SS (RSS) and their time and space complexity calculation on various benchmark and real-world networks. We find that the RSS based on using QR decomposition instead of SVD in deterministic SS is the fastest. We evaluate the correctness and the performance speed after running these randomized SS heuristics on test networks and comparing the results with deterministic counterpart reported earlier. We find the proposed methods can be used effectively in large and sparse networks; they can be extended to analyse important network structure in dynamically evolving networks owing to their reduced time complexity.