  Searching on bipartite graphs is basal and versatile to many real-world Web
applications, e.g., online recommendation, database retrieval, and
query-document searching. Given a query node, the conventional approaches rely
on the similarity matching with the vectorized node embeddings in the
continuous Euclidean space. To efficiently manage intensive similarity
computation, developing hashing techniques for graph structured data has
recently become an emerging research direction. Despite the retrieval
efficiency in Hamming space, prior work is however confronted with catastrophic
performance decay. In this work, we investigate the problem of hashing with
Graph Convolutional Network on bipartite graphs for effective Top-N search. We
propose an end-to-end Bipartite Graph Convolutional Hashing approach, namely
BGCH, which consists of three novel and effective modules: (1) adaptive graph
convolutional hashing, (2) latent feature dispersion, and (3) Fourier
serialized gradient estimation. Specifically, the former two modules achieve
the substantial retention of the structural information against the inevitable
information loss in hash encoding; the last module develops Fourier Series
decomposition to the hashing function in the frequency domain mainly for more
accurate gradient estimation. The extensive experiments on six real-world
datasets not only show the performance superiority over the competing
hashing-based counterparts, but also demonstrate the effectiveness of all
proposed model components contained therein.
