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For: Hewapathirana IU, Lee D, Moltchanova E, McLeod J. Change detection in noisy dynamic networks: a spectral embedding approach. Soc Netw Anal Min 2020. [DOI: 10.1007/s13278-020-0625-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Number Cited by Other Article(s)
1
Xie Y, Wang W, Shao M, Li T, Yu Y. Multi-view Change Point Detection in Dynamic Networks. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
2
Garg S, Narayanam R, Bandyopadhyay S. A framework to preserve distance-based graph properties in network embedding. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-021-00795-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
3
Correspondence analysis-based network clustering and importance of degenerate solutions unification of spectral clustering and modularity maximization. SOCIAL NETWORK ANALYSIS AND MINING 2020. [DOI: 10.1007/s13278-020-00686-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
4
Hossny AH, Mitchell L, Lothian N, Osborne G. Feature selection methods for event detection in Twitter: a text mining approach. SOCIAL NETWORK ANALYSIS AND MINING 2020. [DOI: 10.1007/s13278-020-00658-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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