The rapid growth of digital scholarly content has made citation recommendation central to literature discovery. This survey organizes the field through a graph-centric lens, unifying unipartite, bipartite, and k-partite formulations and the typed relations that connect papers, authors, venues, concepts, and time. Despite growing research, several critical gaps remain in citation recommendation: (i) heterogeneous relations are underutilized, limiting the ability to model complex interactions; (ii) temporal dynamics and real-time updates are rarely incorporated, restricting adaptivity; (iii) social and serendipity signals (e.g., co-reading, communities, novelty/diversity) are largely neglected, reducing user-centric utility; (iv) cross-lingual and cross-domain transfer is underexplored, limiting global applicability; and (v) evaluation practices remain fragmented, emphasizing accuracy over transparency, diversity, and robustness. We consolidate datasets, tasks, and metrics, and outline a roadmap emphasizing relation-weighted heterogeneous graphs, temporally adaptive learners, cross-lingual alignment, explanation-aware objectives, and deployable pipelines with open artifacts. This graph-centric perspective surfaces actionable gaps and opportunities for building citation recommenders that are accurate, transparent, and production-ready.
Kefalas et al. (Mon,) studied this question.