Co-citation linkages are commonly used to measure implicit relationships between documents. Because traditional co-citation techniques have the drawback of treating the strengths of co-citation linkages equivalent within a single citing document, the analyses of citation contexts have proposed as solutions. Although an analysis that uses embeddings is one of the most promising techniques, such analysis has only been applied to co-citation linkages that are actually observed in citing documents. This study proposes a technique that identifies potential co-citation linkages via context-aware citation network embeddings. A potential co-citation linkage refers to a relationship between two documents of which one document is directly cited by a citing document, whereas the other is not; the uncited document is recommended to be cited in the same citing document based on the context-aware citation network embeddings. This study empirically evaluates the search performance of the proposed methods using potential co-citation linkages. The experimental results revealed that best-performing method outperforms baselines that target only co-citation linkages observed in the citing documents. Furthermore, the results indicated that the proposed method can appropriately incorporate potential co-citation linkages and can obtain relevant documents that are not identified by traditional co-citation searches.