While representation learning has yielded a great success on many graph learning tasks, there is little understanding behind the structures that are being captured by these embeddings. For example, we wonder if the topological features, such as the Triangle Count, the Degree of the node and other centrality measures are concretely encoded in the embeddings. Furthermore, we ask if the presence of these structures in the embeddings is necessary for a better performance on the downstream tasks, such as clustering and classification. To address these questions, we conduct an extensive empirical study over three classes of unsupervised graph embedding models and seven different variants of Graph Autoencoders. Our results show that five topological features: the Degree, the Local Clustering Score, the Betweenness Centrality, the Eigenvector Centrality, and Triangle Count are concretely preserved in the first layer of the graph autoencoder that employs the SUM aggregation rule, under the condition that the model preserves the second-order proximity. We supplement further evidence for the presence of these features by revealing a hierarchy in the distribution of the topological features in the embeddings of the aforementioned model. We also show that a model with such properties can outperform other models on certain downstream tasks, especially when the preserved features are relevant to the task at hand. Finally, we evaluate the suitability of our findings through a test case study related to social influence prediction.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection
Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学院）；Department of Computer Science and Technology, SUNY Empire State University（SUNY埃默里州立大学计算机科学与技术系）
Learning the Ising Model with Generative Neural Networks
Institute of Neuroinformatics, University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）；UCLA（加州大学洛杉矶分校）；Institute for Theoretical Physics, ETH Zurich（苏黎世联邦理工学院理论物理研究所）；Center of Economic Research, ETH Zurich（苏黎世联邦理工学院经济研究中心）
DORA: Exploring Outlier Representations in Deep Neural Networks
Leibniz Institute for Agriculture and Bioeconomy (ATB)（莱布尼茨农业与生物经济研究所（ATB））；Technical University of Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））；Korea University（高丽大学）；Max Planck Institut für Informatik（马克斯·普朗克信息学研究所）；Google Research（谷歌研究院）；University of Potsdam（波茨坦大学）；UiT Arctic University of Norway（挪威北极大学）
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
University of California, Riverside（加州大学河滨分校）；Argonne National Laboratory（阿贡国家实验室）；Washington State University（华盛顿州立大学）；Missouri University of Science and Technology（密苏里科技大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
Advanced Light Source, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室先进光源）；Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室能源研究应用高级数学中心）；Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室分子生物物理学与综合生物成像部）；Berkeley Synchrotron Infrared Structural Biology program, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室伯克利同步辐射红外结构生物学项目）；Materials Sciences Division, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室材料科学部）；McKetta Department of Chemical Engineering, University of Texas（德克萨斯大学麦凯塔化学工程系）
针对X射线散射数据离线探索和实时分析两大挑战，训练领域特定注意力卷积变分自编码器（C-VAE），学习低维表示以捕捉结构变化，并集成到MLExchange平台的Latent Space Explorer中，支持交互式结构探索。
Disentangling Dense Embeddings with Sparse Autoencoders
The Australian National University（澳大利亚国立大学）；Stanford University（斯坦福大学）；Columbia University（哥伦比亚大学）；Space Telescope Science Institute（空间望远镜科学研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）
SAE-FD: Sparse Autoencoder Feature Distillation for Continual Learning of Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；The 63rd Research Institute, National University of Defense Technology, Nanjing（国防科技大学第六十三研究所，南京）
THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation
Data Science Degree Program National Taiwan University；Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan；University of Illinois Urbana-Champaign Electrical \& Computer Engineering Illinois United States；Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan；Department of Electrical Engineering National Taiwan University Taipei Taiwan；National Taiwan University；University of Illinois Urbana-Champaign
Exploring Diverse Methods in Visual Question Answering
University of Michigan（密歇根大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Chongqing University（重庆大学）；Columbia University（哥伦比亚大学）；University of Chicago（芝加哥大学）
SegCompass: Exploring Interpretable Alignment with Sparse Autoencoders for Enhanced Reasoning Segmentation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Meituan, Beijing（美团，北京）；University of Chinese Academy of Sciences（中国科学院大学）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）
Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders
BrainCapture；DTU Health Tech（技术大学丹麦健康技术）；DTU Compute（技术大学丹麦计算）；Department of Biomedical Data Science（生物医学数据科学系）；Department of Computer Science（计算机科学系）；Seer Medical（Seer医疗）；Filadelfia Epilepsy Hospital（菲拉德尔菲亚癫痫医院）；University Hospital of Copenhagen（哥本哈根大学医院）
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations
Gatsby Computational Neuroscience Unit, University College London（伦敦大学学院盖茨比计算神经科学单元）；Department of Mathematics and Center for Scientific Computation and Mathematical Modeling, University of Maryland College Park（马里兰大学帕克分校数学系与科学计算与数学建模中心）
