Abstract
Many network-based tasks need powerful feature expression to capture the diversity of networks. It can be provided by network embedding learning of nodes. The related researches led to a significant progress so far. Nevertheless, suffering from paying unique attention on the structure or neighborhoods of nodes, most of methods cannot describe the vector representations of nodes well enough. In addition, random walk plays a key role during the learning procedure in many advanced methods. The successor node is selected according t the proximity of its direct prior node in each walk step. It is easy to lead a similarity drifting in a long walk sequence and influence the sequence quality. To address these issues, a fuzzy hierarchical network embedding approach (FHNE) is put forward to learn the vector expression fusing structure and neighbor information. Firstly, a multi-granular graph is constructed by a fuzzy k-core decomposition to encode the structural and neighborhood information of nodes. Then, inspired by the epidemic method, a biased random walk is designed to solve the similarity drifting. Many numerical experiments demonstrate that our method exhibits superior performance on various tasks in some real datasets. It verifies that FHNE can learn the efficient network representations.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.