  Block modeling is widely used in studies on complex networks. The cornerstone
model is the stochastic block model (SBM), widely used over the past decades.
However, the SBM is limited in analyzing complex networks as the model is, in
essence, a random graph model that cannot reproduce the basic properties of
many complex networks, such as sparsity and heavy-tailed degree distribution.
In this paper, we provide an edge exchangeable block model that incorporates
such basic features and simultaneously infers the latent block structure of a
given complex network. Our model is a Bayesian nonparametric model that
flexibly estimates the number of blocks and takes into account the possibility
of unseen nodes. Using one synthetic dataset and one real-world stock ownership
dataset, we show that our model outperforms state-of-the-art SBMs for held-out
link prediction tasks.
