This editorial introduces the first part of the Special Issue on “Advanced Technologies in the Decentralized Web.” As the Internet evolves toward more user-centric and resilient architectures, concepts like Web3 and Web 3.0 have gained significant prominence. This issue explores key advancements including decentralized machine learning, AI-blockchain integration, identity management, and data sovereignty. We provide an overview of the selected papers, highlighting their contributions to creating a more transparent and secure decentralized digital future.

Unlike fungible tokens (e.g., cryptocurrency), a Non-Fungible Token (NFT) is unique and indivisible. As such, they can be used to authenticate ownership of digital assets (e.g., a photo) in a decentralized fashion. Given that NFTs have generated significant media attention since 2021, we perform a large-scale measurement study of the NFT ecosystem. We collect over 242M transfer logs and over 97M marketplace transactions until Aug 1st, 2023, by far the largest NFT dataset, to the best of our knowledge. We characterize the on-chain behavior of NFTs and their trading across five major marketplaces. We find that, although the NFT ecosystem is growing rapidly, it is driven by a relatively small set of dominant centralized players, with suspicious trade activities, e.g., over 23% of the monetary volume is generated by malicious wash trading and the ecosystem has experienced over 157K cases of NFT arbitrage, with a total sum of over \25M profit. Our observations motivate the need for more research efforts in the NFT security analysis.

As Web3 projects leverage airdrops to incentivize participation, airdrop hunters tactically amass wallet addresses to capitalize on token giveaways. This poses challenges to the decentralization goal. Current detection approaches tailored for cryptocurrencies overlook non-fungible tokens (NFTs) nuances. We introduce ARTEMIS, an optimized graph neural network system for identifying airdrop hunters in NFT transactions. ARTEMIS captures NFT airdrop hunters through: (1) a multimodal module extracting visual and textual insights from NFT metadata using Transformer models; (2) a tailored node aggregation function chaining NFT transaction sequences, retaining behavioral insights; (3) engineered features based on market manipulation theories detecting anomalous trading. Evaluated on decentralized exchange Blur's data, ARTEMIS significantly outperforms baselines in pinpointing hunters. This pioneering computational solution for an emergent Web3 phenomenon has broad applicability for blockchain anomaly detection. The data and code for the paper are accessible at the following link: \hrefhttps://doi.org/10.5281/zenodo.10676801 doi.org/10.5281/zenodo.10676801.