#machine learning
Jun 2025
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Extensive experimental results on synthetic and real-world datasets show that the balanced graph learning method outperforms competing methods and enables reuse of spectral filters, wavelets, and graph neural nets constructed for positive graphs.
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka et al.
· arXiv.org · 0 citations