  In this paper, we study how to optimize existing Non-Fungible Token (NFT)
incentives. Upon exploring a large number of NFT-related standards and
real-world projects, we come across an unexpected finding. That is, the current
NFT incentive mechanisms, often organized in an isolated and one-time-use
fashion, tend to overlook their potential for scalable organizational
structures.
  We propose, analyze, and implement a novel reference incentive model, which
is inherently structured as a Directed Acyclic Graph (DAG)-based NFT network.
This model aims to maximize connections (or references) between NFTs, enabling
each isolated NFT to expand its network and accumulate rewards derived from
subsequent or subscribed ones. We conduct both theoretical and practical
analyses of the model, demonstrating its optimal utility.
