| Title | Dynamic Network Models Made By Multiple Probabilistic Mechanism |
| Authors | Wang, Xiaomin Ma, Fei Yao, Bing |
| Affiliation | Peking Univ, Key Lab High Confidence Software Technol, Beijing 100871, Peoples R China Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China Northwest Normal Univ, Coll Math & Stat, Lanzhou 730070, Peoples R China |
| Issue Date | 2020 |
| Publisher | PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020) |
| Abstract | As known, no more attentions are put to multiple stochastic network models, we design multiple probabilistic dynamic network models in this article for dealing with a network having two or more different communities with different growing mechanisms and preferential attachment mechanisms. We propose three construction algorithms for producing three types of models after defining operators consisted of graphs and graph operations, and get three spaces generated by three types of models. We introduce partial technique, such as partial multiple preferential attachment mechanism, partial degree distribution etc. in dealing with topological structures of our models. Multiple probabilistic community network models are particular models, but close to social networks. We distribute problems for further research. |
| URI | http://hdl.handle.net/20.500.11897/603047 |
| ISBN | 978-1-7281-4390-3 |
| Indexed | CPCI-SSH(ISSHP) CPCI-S(ISTP) |
| Appears in Collections: | 高可信软件技术教育部重点实验室 信息科学技术学院 |
