2024 : 3 : 5
Sadoon Azizi

Sadoon Azizi

Academic rank: Associate Professor
Education: PhD.
ScopusId: 65456
Faculty: Faculty of Engineering
Address: Room No. 206, Department of Computer Engineering and Information Technology , Faculty of Engineering , University of Kurdistan, Sanandaj, Iran.


Interlayer Link Prediction in Multiplex Networks by Analyzing Matching Degree
Interlayer link prediction, multilayer networks, inter-layer similarity metric, structural information, node similarity, multiplex network, online social network.
Researchers Sakar Omar Khdir(Student)، Sadoon Azizi(Advisor)، Alireza Abdollahpouri(PrimaryAdvisor)


Complex networks play an important role in modeling and analyzing complex systems such as the social system, biological system and information system. In real world, some networked systems can be better modeled as a multilayer structure, where there are relationships among nodes in multiple layers. Multilayer networks with similar nodes across layers are also known as multiplex networks. Various approaches have been introduced to predict links in networked structures, which can be generally categorized into two classes: similarity-based and learning-based. Link prediction in multiplex network is used to predict interlayer links between layers. Given the structure of a network, a link prediction algorithm obtains a rank of links and identify those that are likely to be spurious, which are established between two non-adjacent nodes between the layers of the network. Interlayer link prediction is used to predict links in one of the layers, taking into account the structural information of other layers. The proposed Interlayer link prediction method in multiplex network aims at identifying whether the accounts in different OSNs belong to the same person ,which may have different usernames, photographs, and profiles. We develop an algorithm that aims to predict links between nodes in high-order network structure and also to improving the accuracy and performance of interlayer link prediction. The algorithm offers the advantages of power-law degree distribution; they also can effectively associate with accounts belong to same user across different network layers. It also predicts link between nodes across different network layers. Experimental results on both synthetic and real-world networks confirm outperformance of the proposed method in terms of prediction accuracy in comparison with similar methods.