  A variety of real-world systems can be modeled as bipartite networks. One of
the most powerful and simple link prediction methods is Linear-Graph
Autoencoder(LGAE) which has promising performance on challenging tasks such as
link prediction and node clustering. LGAE relies on simple linear model w.r.t.
the adjacency matrix of the graph to learn vector space representations of
nodes. In this paper, we consider the case of bipartite link predictions where
node attributes are unavailable. When using LGAE, we propose to multiply the
reconstructed adjacency matrix with a symmetrically normalized training
adjacency matrix. As a result, 2-hop paths are formed which we use as the
predicted adjacency matrix to evaluate the performance of our model.
Experimental results on both synthetic and real-world dataset show our approach
consistently outperforms Graph Autoencoder and Linear Graph Autoencoder model
in 10 out of 12 bipartite dataset and reaches competitive performances in 2
other bipartite dataset.
