Signed graph pre training improves learning with negative and positive links
Signed Graph Pre-Training and Prompt Learning
Machine Learning
Summary
Graphs with positive and negative connections show up in many areas like social networks and biology, but learning from these graphs across different tasks is hard. The authors introduce TopoSIGN, a new system that learns patterns in these signed graphs by looking at their shape and structure in two ways. It combines a special math tool called the magnetic signed Laplacian with another tool that captures complex topological features, making it better at understanding these graphs. Experiments show TopoSIGN helps algorithms learn better from signed graphs and adapt to new problems.
What this means in practice
- •For financial analysts: Improve models analyzing correlations and anti-correlations in financial networks using pretrained signed graph embeddings.
- •For social network engineers: Enhance community detection and trust evaluation in platforms with positive and negative user relationships through transfer learned graph features.
Authors
Zihan Mei, Rong Pan, Yuzhou Chen, Yixuan He
Abstract
Signed graphs arise in trust--distrust networks, financial correlation systems, biological interaction graphs, and many other domains in which edges can be positive or negative and may also be directed. While signed graph neural networks have improved task-specific learning, graph transfer learning on signed graphs remains underdeveloped. In this paper, we introduce TopoSIGN, a pioneer topology-guided graph pre-training and prompt learning framework for signed graphs. TopoSIGN combines a structural encoder built on the magnetic signed Laplacian with a novel persistent-homology branch that summarizes signed topology through Dowker-complex persistence images. The fused embeddings are then transferred to a prompt learning function. Experimental results on synthetic and real-world datasets demonstrate the efficacy of TopoSIGN in extracting useful structural information in signed graphs, as well as the adaptability and flexibility of the proposed general framework.