## 2 Literature Review  
A citation network is a graph, its nodes being articles and its edges citation links between them [24; 25]. In recent years, a number of studies have been conducted to leverage deep neural networks and word embedding to extract entities from web and social network datasets [26; 27; 28; 29; 30]. [PERSON] et al. [31] covered the problem of detecting interlinked author communities within the citation networks and the topics covered by the documents in a collection of data was addressed They employed the topic-link LDA model (for the formation of network links, topic similarity, and community similarity are measured) to uncover networks of the authors and topics using citation data from CiteSeer. The results demonstrate that the proposed approach outperforms the content-based approach, at a 7% F1 measure. Similarly, [PERSON] [32] proposed a co-citation search method that is a combination of graph-based algorithm and co-citation context, comparing it to the traditional co-citation search method without context. For experimentation, they used biomedicine and computer linguistics datasets with a total of 172,734 and 10,921 documents, respectively, and stated that the model with citation context performs better than it does without. [PERSON] and [PERSON] [33] predicted the links between documents and their summarizations using Relational Topic Modelling (RTM: a data model composed of document words and the links between them). The authors worked with datasets from three databases: WebKB (contains web pages of computer science departments at several universities); CORA (Coriolis Ocean Database for Reanalysis); and PNAS (Proceedings of the National Academy of Sciences). Their results showed that RTM models predicted the links between documents with improved precision of 5%, compared to unigram and LDA models. In another study, [PERSON] et al. [34] combined four citation network methods (direct citation, coupling, indirect citation, and co-citation) for a network of multiple types of citation. The results verify that the combination reveals complex information about citations and locates a greater number of influential citations than direct citation-based networks. A comparison of citation-context networks has been performed by [PERSON], [PERSON], and [PERSON] [35]. They retrieved the citation context of articles from the WoS repositories and, using VOSviewer, generated various co-occurrence networks based on articles' keywords, abstracts, and titles. The results show that networks based on keywords from titles and abstracts show the broad range of the research areas covered by the citing article, while networks based on context reveal the cognitive impact of the cited article on the citing work. Citation recommendation systems can be divided into two categories. The first group is global systems, which take the entire scholarly article as their input and recommend the citations for the article. [PERSON] et al. [36] proposed a citation recommendation approach that incorporates citation context, the article's content, and its authorship. They conducted experiments on 12,762 articles published before 2012 from the ACL Anthology Network. The results demonstrated that the suggested recommendation system achieves about 16.2% more MAP than a system that is simply based on query text. [PERSON] et al. [37] designed a recommendation system named \"VOPRec\"; they considered both textual information and network topologies simultaneously. To find similar research topics, textual information is converted into word embedding. Using physics journals as a dataset, the experiments showed that the proposed system achieved better precision, recall, and F1 than the state-of-the-art models (i.e., Doc2 vec and Struc2 vec). Some studies have presented novel techniques for efficient global, as well as context-aware, recommendation systems. [PERSON] et al. [38] designed the citation recommendation system \"ActiveCite\", which uses three approaches: collaborative filtering, content-based analysis, and citation analysis.