  Decentralization is emerging as a key feature of the future Internet.
However, effective algorithms for search are missing from state-of-the-art
decentralized technologies, such as distributed hash tables and blockchain.
This is surprising, since decentralized search has been studied extensively in
earlier peer-to-peer (P2P) literature. In this work, we adopt a fresh outlook
for decentralized search in P2P networks that is inspired by advancements in
dense information retrieval and graph signal processing. In particular, we
generate latent representations of P2P nodes based on their stored documents
and diffuse them to the rest of the network with graph filters, such as
personalized PageRank. We then use the diffused representations to guide search
queries towards relevant content. Our preliminary approach is successful in
locating relevant documents in nearby nodes but the accuracy declines sharply
with the number of stored documents, highlighting the need for more
sophisticated techniques.
