Haeran Cho
Biographic Data
| ID | 3925811 |
|---|---|
| NAME | Haeran Cho |
| GIVEN NAMES | Haeran |
| FAMILY NAME | Cho |
| SIGNATURE | CHO H |
| AFFILIATIONS | University of Bristol |
| ORCID | 0000-0002-0725-8704 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Fnets
We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS co…
Link prediction for interdisciplinary collaboration via co-authorship network
We analyse the Publication and Research data set of University of Bristol collected between 2008 and 2013. Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network
Link prediction for interdisciplinary collaboration via co-authorship network
We analyse the Publication and Research data set of University of Bristol collected between 2008 and 2013. Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network
Link prediction for interdisciplinary collaboration via co-authorship network
We analyse the Publication and Research data set of University of Bristol collected between 2008 and 2013. Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network
Fnets
We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS co…
Artificial Intelligence (2 works) · Complex Network Analysis Techniques (2 works) · Computer Science (2 works) · Advanced Graph Neural Networks (1 works) · Autoregressive model (1 works) · Bioinformatics and Genomic Networks (1 works) · Complex Systems and Time Series Analysis (1 works) · Data mining (1 works) · Data science (1 works) · Data set (1 works)