A Higher-Order Temporal H-Index for Evolving Networks

Author:

Oettershagen Lutz1ORCID,Kriege Nils M.2ORCID,Mutzel Petra3ORCID

Affiliation:

1. KTH Royal Institute of Technology, Stockholm, Sweden

2. University of Vienna, Vienna, Austria

3. University of Bonn, Bonn, Germany

Funder

Vienna Science and Technology Fund

Deutsche Forschungsgemeinschaft

Lamarr-Institute for Machine Learning and Artificial Intelligence

EC H2020 RIA project SoBigData++

ERC Advanced Grant REBOUND

Publisher

ACM

Reference75 articles.

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2. Joonhyun Bae and Sangwook Kim . 2014. Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Physica A: Statistical Mechanics and its Applications , Vol. 395 ( 2014 ), 549--559. Joonhyun Bae and Sangwook Kim. 2014. Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Physica A: Statistical Mechanics and its Applications, Vol. 395 (2014), 549--559.

3. Vladimir Batagelj and Matjaz Zaversnik . 2003. An O(m) algorithm for cores decomposition of networks. arXiv preprint cs/0310049 ( 2003 ). Vladimir Batagelj and Matjaz Zaversnik. 2003. An O(m) algorithm for cores decomposition of networks. arXiv preprint cs/0310049 (2003).

4. Ferenc Béres , Róbert Pálovics , Anna Oláh , and András A. Benczúr . 2018. Temporal walk based centrality metric for graph streams . Applied Network Science , Vol. 3 , 1 ( 2018 ), 32:1--32:26. https://doi.org/10.1007/s41109-018- 0080 - 0085 10.1007/s41109-018-0080-5 Ferenc Béres, Róbert Pálovics, Anna Oláh, and András A. Benczúr. 2018. Temporal walk based centrality metric for graph streams. Applied Network Science , Vol. 3, 1 (2018), 32:1--32:26. https://doi.org/10.1007/s41109-018-0080-5

5. A faster algorithm for betweenness centrality*

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