We introduce Permutation and Structured Perturbation Inference (PSPI), a new problem formulation that abstracts many graph matching tasks that arise in systems biology. PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause. For example, suppose there are two gene regulatory networks X and Y from a diseased and normal tissue respectively. Then, the PSPI problem can be used to detect if there has been a structural change between the two networks which can serve as a signature of the disease. Besides the new problem formulation, we propose an ADMM algorithm (STEPD) to solve a relaxed version of the PSPI problem. An extensive case study on comparative gene regulatory networks (GRNs) is used to demonstrate that STEPD is able to accurately infer structured perturbations and thus provides a tool for computational biologists to identify novel prognostic signatures. A spectral analysis confirms that STEPD can recover small clique-like perturbations making it a useful tool for detecting permutation-invariant changes in graphs.
GraDE: A Graph Diffusion Estimator for Frequent Subgraph Discovery in Neural Architectures
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；BenchCouncil (International Open Benchmark Council)（国际开放基准委员会）；University of Chinese Academy of Sciences（中国科学院大学）；Northwestern University（西北大学）
McGill University（麦吉尔大学）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）；RWTH Aachen University（亚琛工业大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
Cohesive Group Discovery in Interaction Graphs under Explicit Density Constraints
Peking University（北京大学）；Beijing Capital International Airport Co., Ltd.（北京首都国际机场有限公司）；Guangzhou University（广州大学）；Dalian University of Technology（大连理工大学）
GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine
I2DB, Washington University School of Medicine（I2DB，华盛顿大学医学学院）；Department of Computer Science and Engineering, Washington University in St. Louis（计算机科学与工程系，华盛顿大学圣路易斯分校）；Department of Genetics, Washington University School of Medicine（遗传学系，华盛顿大学医学学院）
