Core community structure recovery and phase transition detection in temporally evolving networks
Community detection in time series networks represents a timely and significant research topic due to its applications in a broad range of scientific fields, including biology, social sciences and engineering. In this work, we introduce methodology to address this problem, based on a decomposition o...
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| Published in | Scientific reports Vol. 8; no. 1; pp. 12938 - 16 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
28.08.2018
Nature Publishing Group |
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| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-018-29964-9 |
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| Abstract | Community detection in time series networks represents a timely and significant research topic due to its applications in a broad range of scientific fields, including biology, social sciences and engineering. In this work, we introduce methodology to address this problem, based on a decomposition of the network adjacency matrices into low-rank components that capture the community structure and sparse & dense noise perturbation components. It is further assumed that the low-rank structure exhibits sharp changes (phase transitions) at certain epochs that our methodology successfully detects and identifies. The latter is achieved by averaging the low-rank component over time windows, which in turn enables us to precisely select the correct rank and monitor its evolution over time and thus identify the phase transition epochs. The methodology is illustrated on both synthetic networks generated by various network formation models, as well as the Kuramoto model of coupled oscillators and on real data reflecting the US Senate’s voting record from 1979–2014. In the latter application, we identify that party polarization exhibited a sharp change and increased after 1993, a finding broadly concordant with the political science literature on the subject. |
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| AbstractList | Community detection in time series networks represents a timely and significant research topic due to its applications in a broad range of scientific fields, including biology, social sciences and engineering. In this work, we introduce methodology to address this problem, based on a decomposition of the network adjacency matrices into low-rank components that capture the community structure and sparse & dense noise perturbation components. It is further assumed that the low-rank structure exhibits sharp changes (phase transitions) at certain epochs that our methodology successfully detects and identifies. The latter is achieved by averaging the low-rank component over time windows, which in turn enables us to precisely select the correct rank and monitor its evolution over time and thus identify the phase transition epochs. The methodology is illustrated on both synthetic networks generated by various network formation models, as well as the Kuramoto model of coupled oscillators and on real data reflecting the US Senate’s voting record from 1979–2014. In the latter application, we identify that party polarization exhibited a sharp change and increased after 1993, a finding broadly concordant with the political science literature on the subject. Community detection in time series networks represents a timely and significant research topic due to its applications in a broad range of scientific fields, including biology, social sciences and engineering. In this work, we introduce methodology to address this problem, based on a decomposition of the network adjacency matrices into low-rank components that capture the community structure and sparse & dense noise perturbation components. It is further assumed that the low-rank structure exhibits sharp changes (phase transitions) at certain epochs that our methodology successfully detects and identifies. The latter is achieved by averaging the low-rank component over time windows, which in turn enables us to precisely select the correct rank and monitor its evolution over time and thus identify the phase transition epochs. The methodology is illustrated on both synthetic networks generated by various network formation models, as well as the Kuramoto model of coupled oscillators and on real data reflecting the US Senate's voting record from 1979-2014. In the latter application, we identify that party polarization exhibited a sharp change and increased after 1993, a finding broadly concordant with the political science literature on the subject.Community detection in time series networks represents a timely and significant research topic due to its applications in a broad range of scientific fields, including biology, social sciences and engineering. In this work, we introduce methodology to address this problem, based on a decomposition of the network adjacency matrices into low-rank components that capture the community structure and sparse & dense noise perturbation components. It is further assumed that the low-rank structure exhibits sharp changes (phase transitions) at certain epochs that our methodology successfully detects and identifies. The latter is achieved by averaging the low-rank component over time windows, which in turn enables us to precisely select the correct rank and monitor its evolution over time and thus identify the phase transition epochs. The methodology is illustrated on both synthetic networks generated by various network formation models, as well as the Kuramoto model of coupled oscillators and on real data reflecting the US Senate's voting record from 1979-2014. In the latter application, we identify that party polarization exhibited a sharp change and increased after 1993, a finding broadly concordant with the political science literature on the subject. |
| ArticleNumber | 12938 |
| Author | Bao, Wei Michailidis, George |
| Author_xml | – sequence: 1 givenname: Wei surname: Bao fullname: Bao, Wei organization: Department of Physics, University of Michigan – sequence: 2 givenname: George surname: Michailidis fullname: Michailidis, George email: gmichail@ufl.edu organization: Department of Statistics and the Informatics Institute, University of Florida |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30154531$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1103_PhysRevE_100_032308 crossref_primary_10_1007_s13171_021_00248_1 crossref_primary_10_1007_s41109_019_0119_2 crossref_primary_10_1016_j_physa_2020_125598 crossref_primary_10_1088_1742_6596_1453_1_012109 crossref_primary_10_1038_s41598_023_44791_3 crossref_primary_10_1109_ACCESS_2021_3105692 crossref_primary_10_1109_TSIPN_2019_2942176 |
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| Title | Core community structure recovery and phase transition detection in temporally evolving networks |
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