Aslan, Serpil; Kaya, Buket; Varol, A.; Yazici, A.; Yazici, M.A.; Varol, C.; Aygunes, G.S.; Cotur, A.

Scientific papers are mostly performed by collaboration between research groups. Researchers work in various subjects and in several research areas. Therefore, identifying a strong research group is a quite complex task. To cope with this complex task, we propose a network-based method that predicts the best appropriate collaborations for researchers in a co-authorship network. In this paper, we have constructed a weighted co-authorship network by using a real database of scientific papers. Almost all of the methods in this area use the unweighted network model. But, weighted networks are particularly important when representing the network-based properties. The constructed network consists of nodes and links which represent respectively authors and their collaborations. In these networks, if two researchers are the common authors in the same paper, they are considered connected. Typically, the link weights represent the number of papers that two authors have made collaboratively. We also use local LCP-based algorithms to calculate the betweenness and closeness of the authors in this paper. The experimental results demonstrate the success of the proposed method.

AtıfScopus3·OpenAlex2

İncele

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.

AtıfScopus14·WoS5·OpenAlex9

İncele

Gül, Serpil; Kaya, Mehmet; Kaya, Buket

This paper proposes a link prediction method to estimate what sort of disease risks may have been carried by the patients via surveying their and similar patients' medical histories. It is possible to make future oriented predictions with link prediction by deriving new data within a network structure. It is attempted to estimate the future structure of the network and the relations which will be created or abandoned by the individuals on the basis of the nodes within the network structure and the relations between these nodes. For this purpose, an undirected weighted disease network is first created by using the data of patients which were subjected to Hemogram test at F. rat University Hospital during 2013. In the disease network, each node represents a disease and the links represent a relationship between diseases. By the developed method, then disease risk prediction is made through a proactive approach by determining what sorts of disease risks were carried by the patients applying with certain complaints. Experimental results demonstrate the applicability of the link prediction in these networks.

AtıfWoS11

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