Key papers

• Discrete Diffusion Models: A Unified Framework from Tokenization to Generation (2026)
  Ye Yuan et al.
  10.48
• Cross-country structure of a sexual contact network derived from a commercial-sex review platform (2026)
  Hiromu Ito et al.
  5.89
• cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data (2026)
  Chun-houh Chen et al.
  5.01
• Cluster-Aware Matching via Laplacian Optimal Transport (2026)
  Gabriel Samberg et al.
  5.01
• Distributional Determinantal Point Process for Repulsive Clustering of Distributions (2026)
  Khai Nguyen et al.
  5.01
• Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks (2026)
  Masahiro Tanaka
  5.01
• Graph-Based Correlation Matrix Generation: A Convex Optimization Approach (2026)
  Ali Fakhar (UGA) et al.
  5.01
• EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI (2026)
  Hao Kong et al.
  4.39
• Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis (2026)
  Yapeng Li et al.
  4.39
• Nonlinear Bandit (2026)
  Tianshuo Zheng et al.
  4.39
• Graph Partitioning with Demands: Generalized Conductance and its Applications (2026)
  Micha{\l} Szyfelbein et al.
  4.39
• Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification (2026)
  Matthew Steven P. Toledo et al.
  4.39
• PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference (2026)
  Xutao Wang et al.
  4.39
• Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization (2026)
  Jiaxuan Cheng
  4.39
• Cluster with Auctions for Vector Search (2026)
  Swann Bessa et al.
  4.39
• An Efficient Newton Algorithm for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence (2026)
  Damien Lesens et al.
  4.39
• Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression (2026)
  Soham Jana
  4.39
• Provable diffusion-based posterior sampling for linear inverse problems via DDIM (2026)
  Yuchen Jiao et al.
  4.39
• Adaptive deep nonparametric regression from dependent data under covariate shift (2026)
  William Kengne et al.
  4.39
• Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA (2026)
  Sergio A. Alvarez
  4.39
• Mapping urban segregation through co-residence network reconstruction (2025)
  Marc Sadurn\'i et al.
  2.69
• Causal Graphs, Markov Properties and Do-calculus for Stochastic Differential Equations (2026)
  Philip Boeken et al.
  2.00
• Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams (2026)
  Kaifeng Zhang et al.
  2.00
• Scalable Gaussian process inference via neural feature maps (2026)
  Anthony Stephenson
  1.89
• When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting (2026)
  Diede P. M. van der Hoorn et al.
  1.89
• Revisiting Forest Proximities via Sparse Leaf-Incidence Kernels (2026)
  Adrien Aumon et al.
  1.67
• gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points (2025)
  Marcus M. Noack et al.
  1.61
• Unsupervised Learning for the Elementary Shortest Path Problem (2025)
  Jingyi Chen et al.
  1.39
• Towards Unbiased Federated Graph Learning: Label and Topology Perspectives (2025)
  Zhengyu Wu et al.
  1.17
• RDSA: A Robust Deep Graph Clustering Framework via Dual Soft Assignment (2024)
  Yang Xiang et al.
  0.89
• Mining Statistically Significant Attribute Associations in Attributed Graphs (2016)
  Jihwan Lee et al.
  —
• Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (2017)
  Aleksandar Bojchevski et al.
  —
• Graph Embedding with Rich Information through Heterogeneous Network (2017)
  Guolei Sun et al.
  —
• A simple yet effective baseline for non-attributed graph classification (2018)
  Chen Cai et al.
  —
• GLEE: Geometric Laplacian Eigenmap Embedding (2019)
  Leo Torres et al.
  —
• Parallel Computation of Graph Embeddings (2019)
  Chi Thang Duong et al.
  —
• Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks (2019)
  Guillaume Salha et al.
  —
• Which way? Direction-Aware Attributed Graph Embedding (2020)
  Zekarias T. Kefato et al.
  —
• Novel Edge and Density Metrics for Link Cohesion (2020)
  Cetin Savkli et al.
  —
• Faster Graph Embeddings via Coarsening (2020)
  Matthew Fahrbach et al.
  —
• Joint Use of Node Attributes and Proximity for Semi-Supervised Classification on Graphs (2020)
  Arpit Merchant et al.
  —
• Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks (2021)
  Alice Gatti et al.
  —
• One-Hot Graph Encoder Embedding (2021)
  Cencheng Shen et al.
  —
• Identifying and Mitigating Instability in Embeddings of the Degenerate Core (2022)
  David Liu et al.
  —
• Dual Node and Edge Fairness-Aware Graph Partition (2023)
  Tingwei Liu et al.
  —
• Privacy-Preserving Graph Embedding based on Local Differential Privacy (2023)
  Zening Li et al.
  —
• Weighted Embeddings for Low-Dimensional Graph Representation (2024)
  Thomas Bl\"asius et al.
  —
• Graph Classification Gaussian Processes via Hodgelet Spectral Features (2024)
  Mathieu Alain et al.
  —
• Multi-Scale Node Embeddings for Graph Modeling and Generation (2024)
  Riccardo Milocco et al.
  —