Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks

ArXi:2604.18820v1 Announce Type: cross Recovering latent structure from count data has received considerable attention in network inference, particularly when one seeks both cross-group interactions and within-group similarity patterns in bipartite networks, which is widely used in ecology research. Such networks are often sparse and inherently imperfect in their detection. Existing models mainly focus on interaction recovery, while the induced similarity graphs are much less studied.