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In the era of big scholarly data, citation recommendation is playing an increasingly significant role as it solves information overload issues by automatically suggesting relevant references that align with researchers' interests. Many state-of-the-art models have been utilized for citation recommendation, among which graph-based models have garnered significant attention, due to their flexibility in integrating rich information that influences users' preferences. Co-authorship is one of the key relations in citation recommendation, but it is usually regarded as a binary relation in current graph-based models. This binary modeling of co-authorship is likely to result in information loss, such as the loss of strong or weak relationships between specific research topics. To address this issue, we present a fine-grained method for co-authorship modeling that incorporates the co-author network structure and the topics of their published articles. Then, we design a three-layered graph-based recommendation model that integrates fine-grained co-authorship as well as author-paper, paper-citation, and paper-keyword relations. Our model effectively generates query-oriented recommendations using a simple random walk algorithm. Extensive experiments conducted on a subset of the anthology network data set for performance evaluation demonstrate that our method outperforms other models in terms of both Recall and NDCG.

Exploiting Fine-Grained Co-Authorship for Personalized Citation Recommendation