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.

翻译：二部图上的搜索是许多现实世界网络应用的基础且多用途的技术，例如在线推荐、数据库检索和查询-文档搜索。给定一个查询节点，传统方法依赖于连续欧几里得空间中向量化节点嵌入的相似度匹配。为了高效管理密集的相似度计算，近年来针对图结构数据开发哈希技术已成为一个新兴研究方向。尽管在汉明空间中具有检索效率优势，但先前的工作却面临性能急剧下降的问题。本研究探讨了基于图卷积网络的二部图哈希方法以实现有效的Top-N搜索。我们提出了一种端到端的二部图卷积哈希方法（简称BGCH），该方法包含三个新颖且有效的模块：（1）自适应图卷积哈希，（2）潜在特征分散，以及（3）傅里叶序列化梯度估计。具体而言，前两个模块在散列编码过程中显著保留结构信息以对抗不可避免的信息损失；最后一个模块在频域中开发傅里叶级数分解哈希函数，主要用于更精确的梯度估计。在六个真实世界数据集上的大量实验不仅表明该方法相较于竞争哈希方法的性能优势，还验证了其中所有提出的模型组件的有效性。