{"type":"ScholarlyArticle","title":"Beyond Homophily: Spectrum-Based Graph Pre-Training and Cluster-Augmented Prompt Tuning","authors":[{"name":"Tingting Li","slug":"tingting-li","given":"Tingting","family":"Li","affiliation":""},{"name":"Yonghao Li","slug":"yonghao-li","given":"Yonghao","family":"Li","affiliation":""},{"name":"Xiangkun Wang","slug":"xiangkun-wang","given":"Xiangkun","family":"Wang","affiliation":""},{"name":"Sixiang Chen","slug":"sixiang-chen","given":"Sixiang","family":"Chen","affiliation":""},{"name":"Boyang Fan","slug":"boyang-fan","given":"Boyang","family":"Fan","affiliation":""},{"name":"Lingfei Ren","slug":"lingfei-ren","given":"Lingfei","family":"Ren","affiliation":""},{"name":"Hao Yu","slug":"hao-yu","given":"Hao","family":"Yu","affiliation":""},{"name":"Xin Yang","slug":"xin-yang","given":"Xin","family":"Yang","affiliation":""}],"abstract":"Graph pre-training and prompt tuning provide an effective route to label-efficient node classification by learning transferable backbones and adapting them with lightweight prompts. However, existing pre-train-and-prompt pipelines often generalize poorly across graphs with diverse homophily due to two fundamental limitations: (i) spectral bias, where learned representations overemphasize a single frequency band, and (ii) prompt misalignment, where spatial-domain prompts cannot explicitly modulate critical spectral components and may even disrupt frequency-aware backbones. In this paper, we propose SCA-GPPT, a spectrum-aware framework that unifies Spectrum-based Graph Pre-training and Cluster-Augmented Prompt Tuning. First, we present a systematic spectral analysis of graph prompting, revealing the inherent limitations of spatial-domain prompts in capturing attenuated high-frequency signals. Second, we explicitly learn complementary low- and high-frequency spectral filters via parameterized Chebyshev polynomials and employ band-aware contrastive learning to model diverse spectral patterns during pre-training. For downstream adaptation, we freeze the pre-trained backbone and introduce cluster-augmented prompts that directly adapt Chebyshev coefficients while enforcing global structural consistency, enabling stable few-shot transfer across varying homophily regimes. Extensive experiments on real-world benchmarks demonstrate that SCA-GPPT consistently outperforms strong baselines under both transductive and inductive settings. Our code is available at https://anonymous.4open.science/r/SCA-GPPT-17B2.","doi":"10.24963/ijcai.2026/310","url":"https://ijcai.org/proceedings/2026/310","pdf":"https://ijcai.org/proceedings/2026/0310.pdf","markdown":"https://ijcai.org/proceedings/2026/310.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/310","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":310,"pages":{"first":2789,"last":2797},"keywords":["Data Mining: Mining graphs","Data Mining: Networks","Machine Learning: Few-shot learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}