Multimodal Graph Learning

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis
Jun 17, 2026Hugo Miccinilli, Theo Di PiazzaCancer Survival Prediction Heterogeneous GNNs
Université Paris-Saclay, CentraleSupélec, MICS, France · University of Lyon, INSA Lyon, CREATIS, France
Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs
Jun 12, 2026Sirui Zhang, Xu Wang, Zhengyu Wu +2Cross-Modal Alignment Multimodal Graph Learning
SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
Jun 11, 2026Zhengyu Wu, Xu Wang, Hongchao Qin +4Graph Representation Learning Cross-Modal Alignment
Multimodal Graph Negative Learning
Jun 11, 2026Zhengyu Wu, Xu Wang, Hongchao Qin +4Multimodal Classification Multimodal Graph Learning
Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS
Jun 10, 2026Siyuan Dai, Yang Du, Kun Zhao +6Graph Neural Networks Multimodal Graph Learning
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA. · Department of Computer Science, University of Maryland, College Park, MD · Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA +2
Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
Jun 8, 2026Joyce Karen Pelaez, Siqi Zhang, Hoo Sang KoAdditive Manufacturing Multimodal Graph Learning
Department of Industrial Engineering, Southern Illinois University Edwardsville, Box 1805, Edwardsville, 62026, IL, USA.
POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction
Jun 8, 2026Brandon Smock, Libin Liang, Max Sokolov +4Table Structure Recognition Multimodal Graph Learning
Kensho Technologies
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
Jun 8, 2026Zekai Chen, Miao Zhang, Jiayang Xing +4Multimodal Federated Learning Missing-Modality Learning
Beijing Institute of Technology, Beijing, 100081, China
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
Jun 2, 2026Jaeyoon Sim, Minjae Lee, Guorong Wu +1Graph Neural Networks Alzheimer's Disease Classification
Pohang University of Science and Technology, Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, USA
Advanced Mathematics Learning Behavior Prediction and Academic Early Warning Model Based on Multimodal Data Analysis
May 31, 2026Liu Qiong, Li ZhengboEducational Assessment AI in Education
Moutai Institute,, Renhuai, 564507, Guizhou, China
UniRTL: Unifying Code and Graph for Robust RTL Representation Learning
May 29, 2026Yi Liu, Hongji Zhang, Lei Chen +2Representation Learning Cross-Modal Representation Learning
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR · Noah’s Ark Lab, Huawei, Hong Kong SAR
OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction
May 28, 2026Xin Wang, Linxin Xiao, Yang Yao +1OOD Generalization Multimodal Graph Learning
DCST, BNRist, Tsinghua University · Beijing, China · DCST, Tsinghua University
Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs
May 23, 2026Hao Yan, Xuanru Wang, Jun Yin +3Graph Neural Networks Multimodal Graph Learning
School of Computer Science and Engineering, Central South University, Changsha, China · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China · School of Information and Communication Technology, Griffith University, Brisbane, Australia
Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
May 21, 2026Yiming Xu, Yixuan Liu, Yuhang Zhang +3Medical Image Classification Multimodal Graph Learning
University of Science and Technology of China · Hefei, China
CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials
May 17, 2026Yanjie Li, Jian Xu, Xu-Yao Zhang +4Multimodal Large Language Models Materials Property Prediction
AnnLab, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China. · Zhongguancun Academy, Zhongguancun Academy, Beijing, China. · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China. +3
TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
May 15, 2026Junle Wang, Xingchuang Liao, Wenjun WuSoftware Engineering Multi-Agent Orchestration
School of Artificial Intelligence, Beihang University Beijing, China
Multimodal Graph-based Classification of Esophageal Motility Disorders
May 13, 2026Alexander Geiger, Lars Wagner, Daniel Rueckert +3Multimodal Graph Learning Multimodal Fusion
Technical University of Munich, School of Medicine and Health, TUM University Hospital Rechts der Isar, Research Group MITI, Munich, Germany. · Technical University of Munich, School of Computation, Information and Technology, Chair for AI in Healthcare and Medicine, Munich, Germany. · Imperial College London, Department of Computing, London, UK. +2
Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
May 13, 2026Amjad Seyedi, Lifang He, Songlin Zhao +2Neural Network Interpretability Multimodal Graph Learning
Dept. of Mathematics & Operational Research University of Mons, Mons, Belgium · Dept. of Computer Science & Engineering Lehigh University, Bethlehem, PA, USA · Dept. of Industrial & Systems Engineering Lehigh University, Bethlehem, PA, USA
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
May 12, 2026Sirui Zhang, Haonan Wang, Xunkai Li +5Multimodal Federated Learning Missing-Modality Learning
Beijing Institute of Technology
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
May 12, 2026Zekai Chen, Xun Wu, Xunkai Li +3Multimodal Federated Learning Cross-Modal Representation Learning
Beijing Institute of Technology, Beijing, 100081, China · Minzu University of China, Beijing, 100081, China
CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
May 12, 2026Daohan Su, Hao Liu, Xunkai Li +6Efficient Multimodal Inference Cross-Modal Alignment
Bridging Sequence and Graph Structure for Epigenetic Age Prediction
May 11, 2026Yao Li, Xikun Zhang, Xiaotao Shen +4Multimodal Graph Learning
School of Computing and Information Systems The University of Melbourne · School of Computing Technologies RMIT University · Lee Kong Chian School of Medicine Nanyang Technological University Singapore +1
CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
May 11, 2026Yousef A. Radwan, Yao Li, Qing Qing +5Continual Learning KG Embedding
Technology, Innovation, Entrepreneurship Department King Abdullah University of Science and Technology · School of Computing and Information Systems The University of Melbourne · College of Computer Science and Technology Jilin University +2
Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
May 5, 2026Jingjing Zhou, Yongshuai Yang, Qing Qing +5Privacy-Preserving ML Multimodal Graph Learning
School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China · College of Computer Science and Technology, Jilin University, Changchun 130012, China · School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia +1
Triple Spectral Fusion for Sensor-based Human Activity Recognition
May 4, 2026Ye Zhang, Longguang Wang, Qing Gao +3Graph Signal Processing Human Activity Recognition
School of Electronics and Communication Engineering, the Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen 518107, China · Aviation University of Air Force, Changchun 130012, China · College of Computer Science, Chongqing University, Chongqing 400044, China +1
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
Apr 27, 2026Boyang Fan, Hengchuang Yin, Siyu Yi +5Oncology Multimodal Graph Learning
College of Computer Science, Sichuan University, Chengdu, China · Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China · College of Mathematics, Sichuan University, Chengdu, China +1
AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling
Apr 22, 2026Zhenyu Wang, Geyan Ye, Wei Liu +1Multimodal Graph Learning
AI for Life Sciences Lab, Tencent, Shenzhen, China
SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology
Apr 19, 2026Zheng Nie, Ruolin Shen, Xinlei Yu +3Multi-Agent Collaboration Dynamic Graph Learning
National University of Singapore, Singapore · Technical University of Munich, Munich, Germany · Zhejiang University, China
Modality-Guided Mixture of Structured Experts with Entropy-Triggered Routing for Multimodal Recommendation
Feb 24, 2026Ji Dai, Quan Fang, DeSheng CaiSparse Mixture-of-Experts Recommender Systems
Beijing University of Posts and Telecommunications, China · Beijing University of Posts and Telecommunications, Beijing, China · Tianjin University of Technology, China
EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation
Aug 22, 2025Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng +2Cross-Modal Alignment Multimodal Embedding
College of Computing and Data Science, Nanyang Technological University, Singapore

Jun 17, 2026, cs.LGJ/K move · Enter open · S save

ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis

Hugo Miccinilli, Theo Di Piazza

Université Paris-Saclay, CentraleSupélec, MICS, France · University of Lyon, INSA Lyon, CREATIS, France

Cancer Survival Prediction Heterogeneous GNNs Survival Analysis Oncology Multimodal Survival Analysis Multimodal Graph Learning

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Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high-dimensional nature of multimodal clinical data. While deep survival models have improved predictive performance over classical statistical approaches, existing methods typically rely on static fusion strategies or temporally agnostic modeling, limiting their ability to capture structured clinical workflows. In this work, we propose ChronoSurv, a heterogeneous hierarchical directed graph framework for multimodal survival analysis. ChronoSurv represents patient care as a progression-aware clinical trajectory using directed graphs aligned with key diagnostic steps. A hierarchical topology incorporates fine-grained, coarse, and global representations, further supporting flexible adaptation to missing modalities, while heterogeneous message passing models complex and asymmetric relationships across modalities and clinical steps. Experimental results on two public datasets demonstrate that ChronoSurv achieves state-of-the-art discriminative performance while maintaining statistically reliable calibration. Comprehensive ablation studies further confirm the contribution of each architectural component, highlighting the potential of trajectory-aware graph modeling for multimodal survival prediction.

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