Yazarlar

• Aslan, Serpil(2)
• Kaya, B.(1)
• Kaya, Mehmet(1)

• MakaleAçık Erişim2020Scopus SJRQ1/r/munzur/3711
  Time-aware link prediction based on strengthened projection in bipartite networks

  Aslan, Serpil; Kaya, B.

  The traditional projection models in the bipartite networks involve many node pairs that consist of weak relationships. These node pairs lead to poor quality predictions as well as high computation time. In this paper, to cope with these problems, we firstly propose a novel projection model, which is called “Strengthened Projection Model”. Then, to predict the potential links in the future, we present a new link prediction approach based on the proposed projection model. Thanks to the proposed model, the computation time is shortened, and the high probability predictions are extracted. The majority of the previous works conducted in this area have used the classical proximity measure algorithms that only take into account the current network structure, regardless of when events occur in the network evolution. To overcome these limited methods, in this paper, we also propose a novel proximity measure algorithm that considers the bipartite network evolution. To the best of our knowledge, this is the first attempt that takes into account the time-awareness in bipartite networks. To evaluate the performance of our proposed approach, we conducted experiments on the academic information network. To construct this bipartite network, we collected data from IEEE Xplore. The experimental results show that the success of the proposed method is promising.

  AtıfScopus11·WoS9·OpenAlex10

  İncele
• MakaleAçık Erişim2018Scopus SJRQ1/r/munzur/3590
  Topic recommendation for authors as a link prediction problem

  Aslan, Serpil; Kaya, Mehmet

  Recently, link prediction studies on large-scale and complex networks have particularly become the focus of interest for researchers in various scientific fields. Many complex networks created from the real-world data contain bipartite structure by nature. Bipartite networks are a kind of complex networks that represent the interactions between the different node groups. Almost all of the previous studies on link prediction in bipartite networks focus on using the properties of projection networks to predict the relations between the node pairs. In this study, a novel similarity-based link prediction method based on strengthening weighted projection is proposed to predict the potential links between the authors and the topics in the large-scale bipartite academic information network created from the real-world data. Since information loss occurs when bipartite networks are converted to unimodal networks, it should be noted that when making a link prediction in this paper, both the bipartite network and the information on strengthening unimodal network obtained from the bipartite network are used. To evaluate the proposed method, a bipartite network was first created from a real dataset consisting of authors and their work. Then the method was tested on this network. Experimental results demonstrate that it is possible to obtain faster and more accurate link prediction results by the proposed method.

  AtıfScopus35·WoS25·OpenAlex34

  İncele