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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed preprint describing HOPE, a computational method for heterophilic open-set node classification in graph neural networks. The work proposes algorithmic innovations but has not undergone peer review, and the abstract provides no quantitative performance metrics, dataset sizes, or statistical comparisons sufficient to assess clinical or real-world utility.
Preprint. Intervention: HOPE method: structure-augmented feature initialization, trustworthy neighborhood aggregation, and heterophily-guided pseudo-extrapolation for open-set node classification. Compared with: State-of-the-art models (not named in abstract).
HOPE uses structure-augmented feature initialization to capture multi-hop structural patterns in heterophilic graphs A trustworthy neighborhood aggregation mechanism dynamically filters noisy cross-class neighbors Heterophily-guided pseudo-extrapolation synthesizes pseudo-unknown proxies near structurally ambiguous regions
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This is an unrefereed arXiv preprint presenting a novel machine-learning algorithm for graph neural networks; it reports computational validation but lacks peer review and clinical or real-world evidence of impact.
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Standard open-set node classification methods rely on the homophily assumption, where connected nodes share labels. However, real-world graphs are often heterophilic, exposing the limitations of current methods and posing new challenges to open-set node classification. On the one hand, cross-class connectivity causes representations from different known or unknown classes to become intertwined after aggregation, undermining their discriminative capacity. On the other hand, structural mixture invalidates threshold-based open-set methods and cross-class feature interpolation, leading to unreliable unknown-class rejection. To address these challenges, we propose HOPE, a Heterophily-aware Open-set node classification method with Pseudo-Extrapolation. To adapt open-set graph neural networks (GNNs) to heterophilic scenarios, HOPE uses a structure-augmented feature initialization layer to capture multi-hop structural patterns. Meanwhile, we design a trustworthy neighborhood aggregation mechanism for standard GNNs to dynamically filter noisy cross-class neighbors. To enhance unknown-class rejection, we introduce a heterophily-guided pseudo-extrapolation strategy. It dynamically maintains known-class centers and extrapolates along cross-class neighborhood displacement directions, synthesizing pseudo-unknown proxies near structurally ambiguous regions. Finally, we optimize the network with joint classification and logit margin regularization, routing synthetic proxies into a dedicated rejection slot without imposing geometric margin constraints in the representation space. Extensive experiments on multiple datasets show that HOPE consistently outperforms state-of-the-art models, validating its effectiveness, robustness, and efficiency.
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