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At E-Commerce stores such as Amazon, eBay, and Taobao, the shopping items and the query words that customers use to search for the items form a bipartite graph that captures search behavior. Such a query-item graph can be used to forecast search trends or improve search results. For example, generating query-item associations, which is equivalent to predicting links in the bipartite graph, can yield recommendations that can customize and improve the user search experience. Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a long-tail distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known as disassortative mixing.

To address these two challenges, we go beyond the bipartite …