Nicholas C. Valler
- Statistical and Nonlinear Physics top 5%
- Computer Networks and Communications top 10%
- Artificial Intelligence
- Sociology and Political Science
- Public Health, Environmental and Occupational Health
- Co-authors
- Michalis FaloutsosB. Aditya PrakashChristos FaloutsosDeepayan ChakrabartiIulian NeamtiuXuetao WeiHarsha V. MadhyasthaDavid G. Andersen
- Topics
- Complex Network Analysis Techniques (6 papers)Opinion Dynamics and Social Influence (5 papers)Opportunistic and Delay-Tolerant Networks (3 papers)
- Cited by
- Statistical and Nonlinear PhysicsModeling and SimulationComputer Networks and Communications
- Journals
- IEEE Journal on Selected Areas in CommunicationsACM SIGCOMM Computer Communication ReviewComputer Networks
- Partner nations
- United StatesSpain
In The Last Decade
Nicholas C. Valler
11 papers receiving 322 citations
Peers
Comparison fields: 5 of 54
- Statistical and Nonlinear Physics 238
- Computer Networks and Communications 95
- Artificial Intelligence 56
- Sociology and Political Science 51
- Public Health, Environmental and Occupational Health 44
Countries citing papers authored by Nicholas C. Valler
This map shows the geographic impact of Nicholas C. Valler's research. 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 Nicholas C. Valler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas C. Valler more than expected).
Fields of papers citing papers by Nicholas C. Valler
This network shows the impact of papers produced by Nicholas C. Valler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicholas C. Valler. The network helps show where Nicholas C. Valler may publish in the future.
Co-authorship network of co-authors of Nicholas C. Valler
This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas C. Valler. A scholar is included among the top collaborators of Nicholas C. Valler based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nicholas C. Valler. Nicholas C. Valler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | 8 | |
| 3 | 4 | |
| 4 | 72 | |
| 5 | 33 | |
| 6 | Spreading processes on networks theory and applications | 4 |
| 7 | 86 | |
| 8 | 5 | |
| 9 | 2 | |
| 10 | 75 | |
| 11 | 0 | |
| 12 | 25 |
About Nicholas C. Valler
Nicholas C. Valler is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Artificial Intelligence, having authored 12 papers that have together received 334 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Opinion Dynamics and Social Influence (5 papers) and Opportunistic and Delay-Tolerant Networks (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (238 citations), Modeling and Simulation (43 citations) and Computer Networks and Communications (95 citations). Nicholas C. Valler has collaborated with scholars based in United States and Spain. Frequent co-authors include Michalis Faloutsos, B. Aditya Prakash, Christos Faloutsos, Deepayan Chakrabarti, Iulian Neamtiu, Xuetao Wei, Harsha V. Madhyastha, David G. Andersen and Michael Butkiewicz. Their work appears in journals such as IEEE Journal on Selected Areas in Communications, ACM SIGCOMM Computer Communication Review and Computer Networks.
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.