Abstract
Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic-aware subnetwork identification is essential to discover potential researchers on particular research topics and provide quality work. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic.
Highlights
Due to the fast-growing volume and variety of online scholarly data, researchers have shown tremendous interest in producing numerous techniques and applications to explore and analyze academic data
3) EVALUATION OBJECTIVES Experiments are performed for the following main objectives: 1) Topic-oriented collaborator identification, which evaluates the effectiveness of the proposed method on identifying researchers for a particular research topic; 2) Collaboration intensity, which assesses the level of intensity the candidate researchers have to each other in terms of collaboration; 3) Accuracy of key subnetwork identification, in terms of identifying collaborators from similar research areas of interest in a subnetwork
If a researcher wants to form a scientific team for a particular research topic, s/he first needs to identify collaborators who have experiences on that specific topic
Summary
Due to the fast-growing volume and variety of online scholarly data, researchers have shown tremendous interest in producing numerous techniques and applications to explore and analyze academic data. Since academic networks are bigger and uphold a highly temporal characteristic, analyzing them causes significant challenges to data mining techniques. Managing a big network is challenging; dividing the big network into subnetworks provides significant insights towards its function and structure. Community structure detection is one of the fundamental applications that help analyze a big network [3], [4]. It helps to solve the discovery of subnetworks of nodes densely linked to one another than the rest of the network [5].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
