Structural reducibility of multilayer networks
- Journal
- Nature Communications
In The Last Decade
doi.org/10.1038/ncomms7864 →Countries where authors are citing Structural reducibility of multilayer networks
This map shows the geographic impact of Structural reducibility of multilayer networks. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Structural reducibility of multilayer networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Structural reducibility of multilayer networks more than expected).
Fields of papers citing Structural reducibility of multilayer networks
This network shows the impact of Structural reducibility of multilayer networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Structural reducibility of multilayer networks.
About Structural reducibility of multilayer networks
This paper, published in 2015, received 396 indexed citations . Written by Manlio De Domenico, Vincenzo Nicosia, Àlex Arenas and Vito Latora covering the research area of Statistical and Nonlinear Physics and Molecular Biology. It is primarily cited by scholars working on Statistical and Nonlinear Physics (277 citations), Molecular Biology (86 citations) and Artificial Intelligence (85 citations). Published in Nature Communications.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1038/ncomms7864.