arXiv:2610.07981v1 Announce Type: new
Abstract: Higher-order models (e.g., hypergraph neural networks) often outperform lower-order baselines on hypergraph learning benchmarks, and their advantages a...
By Fanchen Bu, Fan Li, Geon Lee, Sunwoo Kim, Xiaoyang Wang, Renaud Lambiotte, Kijung Shin
The paper introduces Ra‑NEM, a method that optimizes the area under insertion and deletion curves to improve the faithfulness of feature importance attributions in machine learning models. By deriving an objective function from faithfulness and approximating its gradient, the authors link insertion curves to top‑k feature selection, enabling a loss function that measures attribution quality. Ra‑NEM, integrated with the neural explanation mask framework, works with any differentiable model, yielding accurate, robust, and fast attributions that outperform existing algorithms on multiple XAI metrics.
By Bj{\o}rn Leth M{\o}ller, Bulat Ibragimov, Christian Igel
The paper introduces a systematic benchmark for evaluating explainable methods that attribute temporal interactions in sequential recommendation systems. Using a dual-model masking metric, it assesses ten XAI techniques across CNN, Transformer, SASRec, and BERT4Rec backbones on KuaiRand and MovieLens datasets, revealing that gradient-based methods like GradientSHAP and Integrated Gradients are the most faithful and robust. It also finds that raw attention weights are unreliable, while gradient-weighted attention works better on short sequences but degrades on longer horizons, and that faithful methods capture genuine task structure rather than recency or popularity bias.
By Akash Pandey, Kanisha Shah, Addrish Roy, Dwipam Katariya, Hongyangyang Shi, Amanda Ding, Kalanand Mishra, Pranab Mohanty
arXiv:2606. 06397v1 Announce Type: new Abstract: Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlying structure.
By Shuo Wang, Xiangyu Wang, Quanxin Wang, Bailin Wu, Bokui Wang, Shunyang Huang, Boyan Deng, Haonan Liu, Ruiyi Fang, Zhenxiang Xu, Boyu Wang, Zhao Kang
arXiv:2610.20534v1 Announce Type: new
Abstract: Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as...
By Viet The Nguyen, Vijay Prakash Dwivedi, Gunther Gust
The paper investigates the phenomenon of explanation multiplicity in SHAP, showing that SHAP explanations can vary significantly across repeated runs even when the model, input, and prediction remain unchanged. The authors introduce a dual‑seed evaluation protocol, a hierarchy of metrics, and null models to quantify this variability, finding that multiplicity is widespread across datasets, models, and sampling strategies. They also demonstrate that common metrics like L2 distance may underestimate instability, while rank‑based metrics reveal substantial changes in top‑ranked features, and that improved sampling methods do not fully eliminate run‑to‑run variation.
By Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt, Steven Euijong Whang, Julia Stoyanovich