Jefrey Lijffijt
- Artificial Intelligence top 10%
- Statistical and Nonlinear Physics top 10%
- Information Systems top 10%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Tijl De BiePanagiotis PapapetrouKai PuolamäkiTerttu NevalainenTanja SäilyStefan Τh. GriesRaúl Santos‐RodríguezXi Chen
- Topics
- Complex Network Analysis Techniques (14 papers)Data Mining Algorithms and Applications (10 papers)Advanced Graph Neural Networks (10 papers)
- Partner nations
- BelgiumFinlandUnited Kingdom
In The Last Decade
Jefrey Lijffijt
44 papers receiving 314 citations
Peers
Comparison fields: 5 of 104
- Artificial Intelligence 142
- Statistical and Nonlinear Physics 57
- Information Systems 55
- Signal Processing 50
- Computer Vision and Pattern Recognition 48
Countries citing papers authored by Jefrey Lijffijt
This map shows the geographic impact of Jefrey Lijffijt'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 Jefrey Lijffijt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jefrey Lijffijt more than expected).
Fields of papers citing papers by Jefrey Lijffijt
This network shows the impact of papers produced by Jefrey Lijffijt. 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 Jefrey Lijffijt. The network helps show where Jefrey Lijffijt may publish in the future.
Co-authorship network of co-authors of Jefrey Lijffijt
This figure shows the co-authorship network connecting the top 25 collaborators of Jefrey Lijffijt. A scholar is included among the top collaborators of Jefrey Lijffijt 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 Jefrey Lijffijt. Jefrey Lijffijt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | Quantifying and reducing imbalance in networks | 2 |
| 7 | 6 | |
| 8 | 2 | |
| 9 | 2 | |
| 10 | Scalable Dyadic Independence Models with Local and Global Constraints | 1 |
| 11 | 31 | |
| 12 | Conditional Network Embeddings. | 2 |
| 13 | The normalized Friedkin-Johnsen model (a work-in-progress report) | 0 |
| 14 | 36 | |
| 15 | 2 | |
| 16 | 3 | |
| 17 | 59 | |
| 18 | A fast and simple method for mining subsequences with surprising event counts | 1 |
| 19 | CEECing the baseline: lexical stability and significant change in a historical corpus | 8 |
| 20 | 26 |
About Jefrey Lijffijt
Jefrey Lijffijt is a scholar working on Signal Processing, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 50 papers that have together received 337 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (14 papers), Data Mining Algorithms and Applications (10 papers) and Advanced Graph Neural Networks (10 papers). The work is most often cited by research in Signal Processing (50 citations), Statistical and Nonlinear Physics (57 citations) and Artificial Intelligence (142 citations). Jefrey Lijffijt has collaborated with scholars based in Belgium, Finland and United Kingdom. Frequent co-authors include Tijl De Bie, Panagiotis Papapetrou, Kai Puolamäki, Terttu Nevalainen, Tanja Säily, Stefan Τh. Gries, Raúl Santos‐Rodríguez, Xi Chen, Heikki Mannila and Jaakko Hollmén. Their work appears in journals such as IEEE Access, ACM Computing Surveys and IEEE Transactions on Knowledge and Data Engineering.
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.