Title: PerturbFormer: Adversarial Graph Transformers for Scalable and Resilient Representation Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Adversarial Graph Learning, Transformer Architectures, Multi-scale Embeddings, Generative Pretraining, Adaptive Signal Calibration, Vertex Classification, Computational Efficiency

Abstract: We propose PerturbFormer, a unified framework for graph representation learning that integrates multi-scale structural embeddings, contrastive self-supervised pretraining, and heterophily-aware transformer architectures. To address challenges posed by sparse features and noisy topologies, the model introduces adversarial propagation, where generative modules synthesize structural perturbations and discriminators enforce semantic consistency. The framework leverages degree-normalized attention and relative positional encoding to adaptively model diverse graph environments. Efficiency is achieved through graph-free condensation and hardware-aware optimization. Empirical evaluations demonstrate strong performance across homophilous and heterophilous benchmarks, with enhanced robustness under structural noise and temporal dynamics. This work contributes a scalable and resilient solution for graph-based learning in complex domains.

TL;DR: PerturbFormer trains robust graph transformers by having a GAN perturb edges while a confidence-aware residual module self-corrects, beating SOTA on large homophilous and heterophilous benchmarks with fewer parameters.