inis 19(19): e2

Research Article

Centrality-Based Paper Citation Recommender System

Download939 downloads
  • @ARTICLE{10.4108/eai.13-6-2019.159121,
        author={Abdul  Samad and Muhammad  Arshad Islam and Muhammad  Azhar Iqbal and Muhammad  Aleem},
        title={Centrality-Based Paper Citation Recommender System},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        keywords={Citation Recommendation, Textual Similarity, Topological Similarity},
  • Abdul Samad
    Muhammad Arshad Islam
    Muhammad Azhar Iqbal
    Muhammad Aleem
    Year: 2019
    Centrality-Based Paper Citation Recommender System
    DOI: 10.4108/eai.13-6-2019.159121
Abdul Samad1, Muhammad Arshad Islam2, Muhammad Azhar Iqbal1, Muhammad Aleem1
  • 1: Capital University of Science and Technology, Islamabad, Pakistan
  • 2: FAST-National University of Computer and Emerging Sciences, Islamabad, Pakistan


Researchers cite papers in order to connect the new research ideas with previous research. For the purpose of finding suitable papers to cite, researchers spend a considerable amount of time and effort. To help researchers in finding relevant/important papers, we evaluated textual and topological similarity measures for citation recommendations. This work analyzes textual and topological similarity measures (i.e., Jaccard and Cosine) to evaluate which one performs well in finding similar papers? To find the importance of papers, we compute centrality measures (i.e., Betweeness, Closeness, Degree and PageRank). After evaluation, it is found that topological-based similarity via Cosine achieved 85.2% and using Jaccard obtained 61.9% whereas textualbased similarity via Cosine on abstract obtained 68.9% and using Cosine on title achieved 37.4% citation links. Likewise, textual-based similarity via Jaccard on abstract obtained 35.4% and using Jaccard on title achieved 28.3% citation links.