KDD : proceedings. International Conference on Knowledge Discovery & Data Mining最新文献

Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes. 用间隔原型预训练流行病时间序列预报员。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2026-08-08 DOI: 10.1145/3770855.3818928

Zewen Liu, Juntong Ni, Bohan Wang, Max S Y Lau, Wei Jin

{"title":"Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes.","authors":"Zewen Liu, Juntong Ni, Bohan Wang, Max S Y Lau, Wei Jin","doi":"10.1145/3770855.3818928","DOIUrl":"10.1145/3770855.3818928","url":null,"abstract":"Accurate epidemic forecasting is crucial for outbreak preparedness, but existing data-driven models are often brittle. Typically trained on a single pathogen, they struggle with data scarcity during new outbreaks and fail under distribution shifts caused by viral evolution or interventions. However, decades of surveillance data and the design of various compartmental models from diverse diseases offer an untapped source of transferable knowledge. To leverage the collective lessons from history, we propose CAPE, the first open-source pre-trained model for epidemic forecasting. Unlike existing time series foundation models that overlook epidemiological challenges, CAPE models epidemic dynamics as mixtures of latent compartmental population states, termed compartmental prototypes. It models a flexible dictionary of compartment prototypes directly from a large collection of simulation data, enabling each outbreak to be expressed as a time-varying mixture that links observed infections to latent population states. To promote robust generalization, CAPE adopts the next-token-prediction paradigm during pre-training with lightweight epidemic-aware regularization that aligns the learned prototypes with epidemiological semantics. On a comprehensive benchmark spanning 17 diseases, CAPE significantly outperforms strong baselines with zero-shot forecasting. This work represents a principled step toward pre-trained epidemic models that are both transferable and epidemiologically grounded. We provide our code in: https://github.com/nuuuh/CAPE.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2026 v2","pages":"11540-11551"},"PeriodicalIF":0.0,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13464487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148723946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Why Only 3D for 3D? 2D-Guided Supervision for 3D Medical Vision-Language Models. 为什么只有3D的3D？3D医学视觉语言模型的2d引导监督。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2026-08-01 Epub Date: 2026-08-08 DOI: 10.1145/3770855.3818970

Qilong Zhao, Ziyuan Qin, Liang Zhao

{"title":"Why Only 3D for 3D? 2D-Guided Supervision for 3D Medical Vision-Language Models.","authors":"Qilong Zhao, Ziyuan Qin, Liang Zhao","doi":"10.1145/3770855.3818970","DOIUrl":"10.1145/3770855.3818970","url":null,"abstract":"Training 3D medical vision-language models (VLMs) requires paired volumetric scans and expert annotations such as reports or segmentation masks, which are costly and limited. Meanwhile, large collections of unlabeled 3D scans and strong 2D medical VLMs are increasingly available. We study a simple paradigm: using pretrained 2D models as automatic annotators to supervise 3D VLMs. A 2D teacher generates slice-level descriptions or masks that are aggregated into volume-level pseudo-labels to train a 3D student operating on full volumes at inference. We instantiate this 2D-to-3D supervision framework for report generation and segmentation. In label-scarce regimes, 2D-derived pseudo-labels significantly improve data efficiency. For report generation, pseudo-reports outperform or match ground truth when reports are limited and remain competitive at larger scales. For segmentation, pseudo-masks enable learning from unlabeled volumes and further improve performance when combined with limited expert masks. Overall, strong 2D models can act as scalable annotators that complement scarce 3D labels. This data-centric perspective offers a practical path to scaling 3D medical VLMs by leveraging abundant 2D expertise without changing model architectures.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2026 v 2","pages":"12997-13007"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13502129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation. 基于ICE - g计算的深度双去偏纵向效应估计。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2026-01-01 Epub Date: 2026-08-08 DOI: 10.1145/3770855.3818925

Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang

{"title":"Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation.","authors":"Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang","doi":"10.1145/3770855.3818925","DOIUrl":"10.1145/3770855.3818925","url":null,"abstract":"Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback. While Iterative Conditional Expectation (ICE) G-computation offers a principled approach, its recursive structure suffers from error propagation, corrupting the learned outcome regression models. We propose D 3 -Net, a framework that mitigates error propagation in ICE training and then applies a robust final correction. First, to interrupt error propagation during learning, we train the ICE sequence using Sequential Doubly Robust (SDR) pseudo-outcomes, which provide bias-corrected targets for each regression. Second, we employ a multi-task transformer with a covariate simulator head for auxiliary supervision, regularizing representation learning, and a target network to stabilize training dynamics. For the final estimate, we discard the SDR correction and instead use the uncorrected nuisance models to perform Longitudinal Targeted Minimum Loss-Based Estimation (LTMLE) on the original outcomes. This second-stage, targeted debiasing ensures robustness and optimal finite-sample properties. Comprehensive experiments demonstrate that our model, <math> <msup><mrow><mi>D</mi></mrow> <mrow><mn>3</mn></mrow> </msup> </math> -Net, robustly reduces bias and variance across different horizons, counterfactuals, and time-varying confoundings, compared to existing state-of-the-art ICE-based estimators.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2026 v2","pages":"10646-10657"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability. MOSIC：基于多约束的模型不可知最优子群识别。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2026-01-01 Epub Date: 2026-08-08 DOI: 10.1145/3770855.3818926

Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang

{"title":"MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability.","authors":"Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang","doi":"10.1145/3770855.3818926","DOIUrl":"https://doi.org/10.1145/3770855.3818926","url":null,"abstract":"Current subgroup identification methods typically follow a two-step approach: first estimate conditional average treatment effects (CATEs) and then apply thresholding or rule-based procedures to define subgroups. While intuitive, this decoupled approach fails to incorporate key constraints essential for real-world clinical decision-making-such as subgroup size and propensity overlap. These constraints operate on fundamentally different axes than CATE estimation and are not naturally accommodated within existing frameworks, thereby limiting the practical applicability of these methods. We propose a unified optimization framework that directly solves the primal constrained optimization problem to identify optimal subgroups. Our key innovation is a reformulation of the constrained primal problem as an unconstrained differentiable min-max objective, solved via a gradient descent-ascent algorithm. We theoretically establish that our solution converges to a feasible and locally optimal solution. Unlike threshold-based CATE methods that apply constraints as post-hoc filters, our approach enforces them directly during optimization. The framework is model-agnostic, compatible with a wide range of CATE estimators, and extensible to additional constraints like cost limits or fairness criteria. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness in identifying high-benefit subgroups while maintaining better satisfaction of constraints.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2026 v2","pages":"10658-10668"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13655699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data. 通过模拟真实世界患者数据的临床试验实现精确药物再利用的深度亚分组框架。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-07-20 DOI: 10.1145/3690624.3709418

Seungyeon Lee, Ruoqi Liu, Feixiong Cheng, Ping Zhang

{"title":"A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data.","authors":"Seungyeon Lee, Ruoqi Liu, Feixiong Cheng, Ping Zhang","doi":"10.1145/3690624.3709418","DOIUrl":"10.1145/3690624.3709418","url":null,"abstract":"Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Most existing drug repurposing studies using real-world patient data often treat the entire population as homogeneous, ignoring the heterogeneity of treatment responses across patient subgroups. This approach may overlook promising drugs that benefit specific subgroups but lack notable treatment effects across the entire population, potentially limiting the number of repurposable candidates identified. To address this, we introduce STEDR, a novel drug repurposing framework that integrates subgroup analysis with treatment effect estimation. Our approach first identifies repurposing candidates by emulating multiple clinical trials on real-world patient data and then characterizes patient subgroups by learning subgroup-specific treatment effects. We deploy STEDR to Alzheimer's Disease (AD), a condition with few approved drugs and known heterogeneity in treatment responses. We emulate trials for over one thousand medications on a large-scale real-world database covering over 8 million patients, identifying 14 drug candidates with beneficial effects to AD in characterized subgroups. Experiments demonstrate STEDR's superior capability in identifying repurposing candidates compared to existing approaches. Additionally, our method can characterize clinically relevant patient subgroups associated with important AD-related risk factors, paving the way for precision drug repurposing.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 v1","pages":"2347-2358"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations. 通过可重新参数化的子集解释确定细胞命运决定的组合调控基因。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-08-03 DOI: 10.1145/3711896.3737000

Junhao Liu, Pengpeng Zhang, Martin Renqiang Min, Jing Zhang

{"title":"Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations.","authors":"Junhao Liu, Pengpeng Zhang, Martin Renqiang Min, Jing Zhang","doi":"10.1145/3711896.3737000","DOIUrl":"10.1145/3711896.3737000","url":null,"abstract":"Cell fate decisions are highly coordinated processes governed by complex interactions among numerous regulatory genes, while disruptions in these mechanisms can lead to developmental abnormalities and disease. Traditional methods often fail to capture such combinatorial interactions, limiting their ability to fully model cell fate dynamics. Here, we introduce MetaVelo, a global feature explanation framework for identifying key regulatory gene sets influencing cell fate transitions. MetaVelo models these transitions as a black-box function and employs a differentiable neural ordinary differential equation (ODE) surrogate to enable efficient optimization. By reparameterizing the problem as a controllable data generation process, MetaVelo overcomes the challenges posed by the non-differentiable nature of cell fate dynamics. Benchmarking across diverse stand-alone and longitudinal single-cell RNA-seq datasets and three black-box cell fate models demonstrates its superiority over 12 baseline methods in predicting developmental trajectories and identifying combinatorial regulatory gene sets. MetaVelo further distinguishes independent from synergistic regulatory genes, offering novel insights into the gene interactions governing cell fate. With the growing availability of high-resolution single-cell data, MetaVelo provides a scalable and effective framework for advancing developmental biology and therapeutic applications.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 v2","pages":"1823-1832"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12718083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction. SepsisCalc：通过动态时间图构建将临床计算器集成到早期脓毒症预测中。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-07-20 DOI: 10.1145/3690624.3709402

Changchang Yin, Shihan Fu, Bingsheng Yao, Thai-Hoang Pham, Weidan Cao, Dakuo Wang, Jeffrey Caterino, Ping Zhang

{"title":"SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction.","authors":"Changchang Yin, Shihan Fu, Bingsheng Yao, Thai-Hoang Pham, Weidan Cao, Dakuo Wang, Jeffrey Caterino, Ping Zhang","doi":"10.1145/3690624.3709402","DOIUrl":"10.1145/3690624.3709402","url":null,"abstract":"Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA in Figure 1) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 v1","pages":"2779-2790"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11998859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance. MentalChat16K：会话心理健康援助的基准数据集。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-08-03 DOI: 10.1145/3711896.3737393

Jia Xu, Tianyi Wei, Bojian Hou, Patryk Orzechowski, Shu Yang, Ruochen Jin, Rachael Paulbeck, Joost Wagenaar, George Demiris, Li Shen

{"title":"MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance.","authors":"Jia Xu, Tianyi Wei, Bojian Hou, Patryk Orzechowski, Shu Yang, Ruochen Jin, Rachael Paulbeck, Joost Wagenaar, George Demiris, Li Shen","doi":"10.1145/3711896.3737393","DOIUrl":"10.1145/3711896.3737393","url":null,"abstract":"We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between Behavioral Health Coaches and Caregivers of patients in palliative or hospice care. Covering a diverse range of conditions like depression, anxiety, and grief, this curated dataset is designed to facilitate the development and evaluation of large language models for conversational mental health assistance. By providing a high-quality resource tailored to this critical domain, MentalChat16K aims to advance research on empathetic, personalized AI solutions to improve access to mental health support services. The dataset prioritizes patient privacy, ethical considerations, and responsible data usage. MentalChat16K presents a valuable opportunity for the research community to innovate AI technologies that can positively impact mental well-being. The dataset is available at https://huggingface.co/datasets/ShenLab/MentalChat16K and the code and documentation are hosted on GitHub at https://github.com/PennShenLab/MentalChat16K.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 ","pages":"5367-5378"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12520247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors. 健康：基于卫星环境因子的多模式公共卫生数据集。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-08-03 DOI: 10.1145/3711896.3737440

Yuanlong Wang, Pengqi Wang, Changchang Yin, Ping Zhang

{"title":"SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.","authors":"Yuanlong Wang, Pengqi Wang, Changchang Yin, Ping Zhang","doi":"10.1145/3711896.3737440","DOIUrl":"10.1145/3711896.3737440","url":null,"abstract":"Living environments play a vital role in the prevalence and progression of diseases, and understanding their impact on patient's health status becomes increasingly crucial for developing AI models. However, due to the lack of long-term and fine-grained spatial and temporal data in public and population health studies, most existing studies fail to incorporate environmental data, limiting the models' performance and real-world application. To address this shortage, we developed SatHealth, a novel dataset combining multimodal spatiotemporal data, including environmental data, satellite images, all-disease prevalences estimated from medical claims, and social determinants of health (SDoH) indicators. We conducted experiments under two use cases with SatHealth: regional public health modeling and personal disease risk prediction. Experimental results show that living environmental information can significantly improve AI models' performance and temporal-spatial generalizability on various tasks. Finally, we deploy a web-based application to provide an exploration tool for SatHealth and one-click access to both our data and regional environmental embedding to facilitate plug-and-play utilization. SatHealth is now published with data in Ohio, and we will keep updating SatHealth to cover the other parts of the US. With the web application and published code pipeline, our work provides valuable angles and resources to include environmental data in healthcare research and establishes a foundational framework for future research in environmental health informatics.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 ","pages":"5819-5830"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12340727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks. 图ode及其以后：用图神经网络积分微分方程的综合研究。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-08-03 DOI: 10.1145/3711896.3736559

Zewen Liu, Xiaoda Wang, Bohan Wang, Zijie Huang, Carl Yang, Wei Jin

{"title":"Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks.","authors":"Zewen Liu, Xiaoda Wang, Bohan Wang, Zijie Huang, Carl Yang, Wei Jin","doi":"10.1145/3711896.3736559","DOIUrl":"10.1145/3711896.3736559","url":null,"abstract":"Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as powerful tools for learning on graph-structured data, while differential equations provide a principled framework for modeling continuous dynamics across time and space. The intersection of these fields has led to innovative approaches that leverage the strengths of both, enabling applications in physics-informed learning, spatiotemporal modeling, and scientific computing. This survey aims to provide a comprehensive overview of the burgeoning research at the intersection of GNNs and DEs. We will categorize existing methods, discuss their underlying principles, and highlight their applications across domains such as molecular modeling, traffic prediction, and epidemic spreading. Furthermore, we identify open challenges and outline future research directions to advance this interdisciplinary field. A comprehensive paper list is provided at https://github.com/Emory-Melody/Awesome-Graph-NDEs.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 ","pages":"6118-6128"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0