Yazarlar
- Aslan, Serpil(3)
- Kaya, Mehmet(2)
- Adali, E(1)
- Kaya, B.(1)
- Kaya, Buket(1)
Yayınlar
- MakaleAçık Erişim2020Scopus SJRQ1Time-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.
AtıfScopus11WoS9OpenAlex10
İncele - MakaleAçık Erişim2019Scopus SJRQ2Predicting potential links by using strengthened projections in evolving bipartite networks
Aslan, Serpil; Kaya, Buket; Kaya, Mehmet
The link prediction problem can be used for predicting the link changes that are difficult to understand and costly in network science. So far, many different models have been proposed to predict links in the network. Most of the proposed models in the link prediction have focused on the unimodal networks. In this paper, we present a link prediction model that attempts to predict links in the large-scale bipartite networks. The proposed method consists of two stages: The first stage is the extraction of the potential link set. A limitation of the previous works that addressed the link prediction problem in bipartite networks is that only the structural properties of the projected networks that were transformed from the bipartite networks are used to extract the potential links. In this paper, in addition to the structural properties of the projected networks, we take into account the structural properties of the bipartite network. Also, we employ the strengthened projection network model instead of the classical projection network models. The second stage is the calculation of prediction score of each potential link. The classical proximity measures have several disadvantages in dynamic complex networks. When network evolution that considers valuable information between node pairs are ignored, the potential information source is not efficiently used in the link prediction. Therefore, in this stage, we propose a time-aware proximity measure based on social network evolution, instead of classical proximity measures using the current state of the network. To the best of our knowledge, this is the first effort that considers the evolution in bipartite networks. To test the performance of the proposed method, we made some experiments on two real networks that have different frame size. Experimental results show that the success of our method is promising.
AtıfScopus14WoS5OpenAlex9İncele - BildiriYalnız künye2017Link Prediction Methods in Bipartite Networks
Aslan, Serpil; Kaya, Mehmet; Adali, E
Nowadays, social networks, whose application areas are increasing day by day; Such as data mining, protein-gene interaction networks, online social networks, transport networks, academic information networks, and telecommunications networks. Many real-world complex networks have a naturel bipartite structure. In bipartite networks, which have become the focus of many researchers who have been dealing with social network analysis in recent years, connection estimation methods are the basic methods used to understand the structure of these networks. This study is a survey work that describes the connection in a bipartite network that is suitable for the nature of today's huge data networks. In this study, a bipartite network interface is an introductory guide for researchers who plan to work on this subject by examining the internal links and bipartite network projection methods.
AtıfWoS4İncele