Graph neural networks (GNNs) have been extensively employed in node classification. Nevertheless, recent studies indicate that GNNs are vulnerable to topological perturbations, such as adversarial attacks and edge disruptions. Considerable efforts have been devoted to mitigating these challenges. For example, pioneering Bayesian methodologies, including GraphSS and LlnDT, incorporate Bayesian label transitions and topology-based label sampling to strengthen the robustness of GNNs. However, GraphSS is hindered by slow convergence, while LlnDT faces challenges in sparse graphs. To overcome these limitations, we propose a novel label inference framework, TraTopo, which combines topology-driven label propagation, Bayesian label transitions, and link analysis via random walks. TraTopo significantly surpasses its predecessors on sparse graphs by utilizing random walk sampling, specifically targeting isolated nodes for link prediction, thus enhancing its effectiveness in topological sampling contexts. Additionally, TraTopo employs a shortest-path strategy to refine link prediction, thereby reducing predictive overhead and improving label inference accuracy. Empirical evaluations highlight TraTopo's superiority in node classification, significantly exceeding contemporary GCN models in accuracy.
MAPN: Enhancing Heterogeneous Sparse Graph Representation by Mamba-based Asynchronous Aggregation
School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Software, Fudan University（复旦大学软件学院）；Shanghai Key Laboratory of Data Science（上海数据科学重点实验室）
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion
Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization
Heinz College of Information Systems and Public Policy, Carnegie Mellon University（信息系统与公共政策学院，卡内基梅隆大学）；Carnegie Mellon Electricity Industry Center（卡内基梅隆电力产业中心）；Wilton E. Scott Institute for Energy Innovation（威利特·E·斯科特能源创新研究所）；Department of Engineering and Public Policy, Carnegie Mellon Electricity Industry Center（工程与公共政策系，卡内基梅隆电力产业中心）；Department of Electrical and Computer Engineering, Heinz College of Information Systems and Public Policy（电气与计算机工程系，信息系统与公共政策学院）；Department of Integrated Systems Engineering, The Ohio State University（整合系统工程系，俄亥俄州立大学）
Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard
Department of Electrical Engineering（电子工程系）；Institute of Information Science, Academia Sinica（学术院信息科学研究所）；Institute of Artificial Intelligence Innovation（人工智能创新研究所）；Department of Information Management（信息管理系）
Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework
State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Henan Key Laboratory of Information Security（河南省信息安全重点实验室）；National Engineering Research Center for Mobile Network Technologies（移动网络技术国家工程研究中心）；Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments
CentraleSupélec（中央高等电力学院）；IETR - UMR CNRS 6164（电子与电信研究所 - 法国国家科学研究中心联合研究单位6164）
面向无蜂窝大规模 MIMO 的 mMTC 免授权随机接入，提出在 CPU 集中实现的数据驱动稀疏活动检测算法，并验证其抗输入扰动及定点实现性能。
Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；State Grid XiXian New Area Power Supply Company（国家电网西安新区供电公司）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Energy Technology, Aalborg University（奥尔堡大学能源技术研究所）；Illinois Institute of Technology（伊利诺伊理工大学）
Enhancing LLM Steering through Sparse Autoencoder-Based Vector Refinement
LMU Munich（慕尼黑大学）；University of Georgia（佐治亚大学）；Northwestern University（西北大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The Hong Kong Polytechnic University（香港理工大学）
Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes
Wayne State University（韦恩州立大学）；National University of Civil Protection of Ukraine（乌克兰国民警卫队大学）；Cherkasy Medical Academy（切尔卡瑟医学院）；University of Colorado（科罗拉多大学）
Adversarial-Ensemble Kolmogorov Arnold Networks for Enhancing Indoor Wi-Fi Positioning: A Defensive Approach Against Spoofing and Signal Manipulation Attacks
School of Computer Engineering, Kalinga Institute of Industrial Technology（卡林加工业技术学院计算机工程学院）；Meteorological Training Institute, India Meteorological Department（印度气象局气象培训学院）
针对 Wi-Fi 欺骗与信号强度操纵攻击，本文提出基于 KAN 的对抗训练与基线—鲁棒模型集成方法，显著降低室内定位误差并提升系统可靠性。
Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；Agency for Science, Technology and Research (A*STAR)（科技研究局）
Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
Department of Mathematics and Statistics（数学与统计学系）；Georgia State University（佐治亚州立大学）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science（神经影像与数据科学转化研究三机构中心）
