Tim Kaler
Impact in
-
- Complex Network Analysis Techniques
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
Papers in
-
- Software System Performance and Reliability 1
-
- Graph Theory and Algorithms 4
- Co-authors
- Charles E. Leiserson (5 shared papers)Tao B. Schardl (5 shared papers)Giacomo Domeniconi (1 shared paper)Tengfei Ma (1 shared paper)Toyotaro Suzumura (1 shared paper)Hiroki Kanezashi (1 shared paper)Jie Chen (1 shared paper)William Hasenplaugh (3 shared papers)
- Journals
- ACM SIGPLAN Notices (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (4 papers)
- Partner nations
- United States
In The Last Decade
Tim Kaler
9 papers receiving 695 citations
Tim Kaler's Hit Papers
Peers
Comparison fields: 5 of 76
- Statistical and Nonlinear Physics 209
- Artificial Intelligence 471
- Transportation 53
- Computational Mathematics 4
- Computer Vision and Pattern Recognition 133
Countries citing papers authored by Tim Kaler
This map shows the geographic impact of Tim Kaler'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 Tim Kaler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Kaler more than expected).
Fields of papers citing papers by Tim Kaler
This network shows the impact of papers produced by Tim Kaler. 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 Tim Kaler. The network helps show where Tim Kaler may publish in the future.
Co-authors
The 24 scholars most cited alongside Tim Kaler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs Hit paper breakdown → | 2020 | 626 |
| 2 | 2014 | 57 | |
| 3 | 2014 | 10 | |
| 4 | 2016 | 6 | |
| 5 | 2010 | 6 | |
| 6 | 2017 | 3 | |
| 7 | 2017 | 3 | |
| 8 | 2017 | 1 | |
| 9 | 2024 | 1 |
About Tim Kaler
Tim Kaler is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience and Computational Theory and Mathematics, having authored 9 papers that have together received 713 indexed citations. Recurring topics across this work include Graph Theory and Algorithms (4 papers), Advanced Graph Theory Research (2 papers), Complexity and Algorithms in Graphs (2 papers), Functional Brain Connectivity Studies (2 papers), Software System Performance and Reliability (1 paper), Cloud Computing and Resource Management (1 paper), Neural dynamics and brain function (1 paper) and Parallel Computing and Optimization Techniques (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (209 citations), Artificial Intelligence (471 citations), Transportation (53 citations), Computational Mathematics (4 citations) and Computer Vision and Pattern Recognition (133 citations). Tim Kaler has collaborated with scholars based in United States. Frequent co-authors include Charles E. Leiserson, Tao B. Schardl, Giacomo Domeniconi, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Jie Chen, William Hasenplaugh, Timothy R. Peng and Hari Balakrishnan. Their work appears in journals such as ACM SIGPLAN Notices and DSpace@MIT (Massachusetts Institute of Technology).
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.