# HITS hits art

## TL;DR

This paper adapts Kleinberg's HITS algorithm to evaluate artists and collectors in the blockchain art market, providing a new way to profile users and inform investment and marketing strategies.

## Contribution

It introduces a novel application of the HITS algorithm to the art market, specifically for ranking artists and collectors in a blockchain-based platform.

## Key findings

- The proposed metrics are weakly correlated with traditional network measures.
- The method offers potential for profiling active users and guiding investment strategies.
- It demonstrates the applicability of network science to blockchain art markets.

## Abstract

The blockchain art market is partitioned around the roles of artists and collectors and highly concentrated among few prominent figures. We hence propose to adapt Kleinberg's authority/hub HITS method to rate artists and collectors in the art context. This seems a reasonable choice since the original method deftly defines its scores in terms of a mutual recursive relationship between authorities/artists - the miners of information/art, and hubs/collectors - the assemblers of such information/art. We evaluated the proposed method on the collector-artist network of SuperRare gallery, the major crypto art marketplace. We found that the proposed artist and collector metrics are weakly correlated with other network science metrics like degree and strength. This hints the possibility of coupling different measures in order to profile active users of the gallery and suggests investment strategies with different risk/reward ratios for collectors as well as marketing strategies with different targets for artists.