Xie He
I am interested in connecting Network Science with ML and everything else. Whether it is brain data in biology, or social media data, or even if it is something entirely new, I want to study the topolgoical structure of how network could combine data points and link the world together. Specifically, I am interested in predicting the future through link prediction on temporal networks. Recently, my internships have also expanded my interest to how graph topology would be useful in NLP.
Google Scholar | CV | LinkedIn
Xie He
Phd Candidate in Applied Mathematics,
advised by Peter Mucha
Dartmouth College, expected graduation 06/2024
B.S. in Mathematics of Computation, UCLA, 2019
xie.he.gr[at]dartmouth.edu
News and Updates
I am working on cool sampling projects which will be involving 20 different sampling methods and 7 link prediction methods to see how sampling affect prediction results for over 300 datasets.
I will be a research intern at Microsoft this Summer.
One paper under review at Nature Communication: Sequential Stacking Link Prediction Algorithms for Temporal Networks
One paper under review at ACL, but could not state which one due to confidentiality issue >.>
Selected Works
Sequentailly Stacked Model for Link Prediction on Temporal Networks
Sampling Difference for Different Networks
Noisy subgraph Matching
Exact Subgraph Matching
Finding Anomaly in British Transportation Networks