Summary of Efficient Learning Of Balanced Signed Graphs Via Iterative Linear Programming, by Haruki Yokota et al.
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming
by Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka, Gene Cheung
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to learning balanced signed graphs is proposed, which allows for the reuse of well-studied spectral filters designed for positive graphs. The method extends a sparse inverse covariance formulation based on linear programming (LP), called CLIME, by adding linear constraints to enforce consistent signs of edge weights with node polarities. A suitable parameter ρ > 0 is determined using projections on convex sets (POCS) to guarantee LP feasibility. The resulting LP is solved via an off-the-shelf LP solver in O(N^2.055). Experiments on synthetic and real-world datasets show that the method outperforms competing methods and enables the use of spectral filters and graph convolutional networks (GCNs) designed for positive graphs on signed graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand relationships between things is developed, which can help us make better predictions. This method looks at how things are connected and tries to find patterns in those connections. It uses a special kind of math called linear programming to figure out the right way to look at these connections. The results show that this approach works well and can be used with other methods to make even better predictions. |
