Formal verification of designs with multiple properties has been a long-standing challenge for the verification research community. The task of coming up with an effective strategy that can efficiently cluster properties to be solved together has inspired a number of proposals, ranging from structural clustering based on the property cone of influence (COI) to leverage runtime design and verification statistics. In this paper, we present an attempt towards functional clustering of properties utilizing graph neural network (GNN) embeddings for creating effective property clusters. We propose a hybrid approach that can exploit neural functional representations of hardware circuits and runtime design statistics to speed up the performance of Bounded Model Checking (BMC) in the context of multi-property verification (MPV). Our method intelligently groups properties based on their functional embedding and design statistics, resulting in speedup in verification results. Experimental results on the HWMCC benchmarks show the efficacy of our proposal with respect to the state-of-the-art.
ReVEAL: GNN-Guided Reverse Engineering for Formal Verification of Optimized Multipliers
University of Maryland, College Park, USA（美国马里兰大学College Park分校）；TU Wien, Austria（奥地利维也纳技术大学）；NVIDIA（英伟达公司）；Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））
Energy Guided smoothness to improve Robustness in Graph Classification
Sapienza University of Rome, Rome, Italy（罗马萨皮恩扎大学）；CNRS, i3S, Inria（国家科学研究中心、i3S、法国国家信息与自动化技术研究所）；Institute of Information Science and Technologies "Alessandro Faedo" - ISTI-CNR, Pisa, Italy（信息科学与技术研究所"Alessandro Faedo" - ISTI-CNR，比萨，意大利）
Deep reinforcement learning guided graph neural networks for brain network analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Macquarie University（麦考瑞大学）；Beihang University（北京航空航天大学）；National Acoustic Laboratories（国家声学实验室）；CAEIT（中国电子科学研究院）
Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery
Department of Signal Theory and Communications, King Juan Carlos University（信号理论与通信系，国王胡安·卡洛斯大学）；Department of Electrical and Computer Engineering, Rice University（电气与计算机工程系，里奇大学）
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
George Mason University（乔治梅森大学）；GE Healthcare（GE医疗）；University of Pittsburgh（匹兹堡大学）；University of Maryland（马里兰大学）；Southern University of Science and Technology（南方科技大学）；William and Mary（威廉玛丽学院）
Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis
Sichuan University（四川大学）；Chongqing Three Gorges University（重庆三峡学院）；Megvii Inc（旷视科技有限公司）；Skill Training Center of Sichuan Electric Power Corporation of State Grid（国家电网四川省电力公司技能培训中心）；State Grid Sichuan Electric Power Research Institute（国网四川省电力公司电力科学研究院）
Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
University of Pittsburgh（匹兹堡大学）；University of Maryland（马里兰大学）；U.S. Geological Survey（美国地质调查局）；University of Florida（佛罗里达大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
What Functions Can Graph Neural Networks Generate?
University of Pennsylvania（宾夕法尼亚大学）；Yale University（耶鲁大学）
In this paper, we fully answer the above question through a key algebraic condition on graph functions, called \textit{permutation compatibility} , that relates permutations of weights and features of the graph to functional constraints. We prove that: (i) a GNN, as a graph function, is necessarily permutation compatible; (ii) conversely, any permutation compatible function, when restricted on input graphs with distinct node features, can be generated by a GNN; (iii) for arbitrary node features (not necessarily distinct), a simple feature augmentation scheme suffices to generate a permutation …
Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
Department of Computer Science, Virginia Tech, Virginia, USA（弗吉尼亚理工大学计算机科学系）；Department of Computer Science, Manitoba, Canada（曼尼托巴大学计算机科学系）；Department of Computer Science, University of Toledo, Ohio, USA（托莱多大学计算机科学系）；AI Initiative, University of Central Florida, Florida, USA（佛罗里达大学人工智能倡议）
Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data
i2Cat Foundation（i2Cat基金会）；NEC Laboratories Europe（欧洲NEC实验室）；ICREA（加泰罗尼亚研究与高级研究所）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；University of Antwerp（安特卫普大学）
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）
MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy
New York University（纽约大学）；Southern University of Science and Technology（南方科技大学）；Fudan University（复旦大学）；UC Berkeley（加州大学伯克利分校）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）；Amazon Web Services（亚马逊网络服务）
Guided Graph Compression for Quantum Graph Neural Networks
Institute for Particle Physics and Astrophysics, ETH Zurich, 8093 Zurich, Switzerland（粒子物理与天体物理研究所，苏黎世联邦理工学院，瑞士苏黎世）；Computer Science Department, University of Oviedo, 33003 Oviedo, Spain（奥维多大学计算机科学系，西班牙奥维多）；European Organization for Nuclear Research (CERN), CH-1211 Geneva, Switzerland（欧洲核子研究中心（CERN），瑞士日内瓦）
