OpenRaiser/Intern-Atlas huggingface dataset:
SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors
Authors: Yuanlong Wang, Pengqi Wang, Changchang Yin, Ping Zhang
Venue: KDD
Year: 2025

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.
SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking
Authors: Yuanchun Wang, Jifan Yu, Zijun Yao, Jing Zhang, Yuyang Xie, Shangqing Tu, Yiyang Fu, Youhe Feng, Jinkai Zhang, Jingyao Zhang, Bowen Huang, Yuanyao Li, Huihui Yuan, Lei Hou, Juanzi Li, Jie Tang
Venue: KDD
Year: 2025

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable code. To evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://github.com/RUCKBReasoning/SoAy.
Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness
Authors: Xiong Wang, Yi Zhang, Yuxin Chen, Yuqing Li, Chuanhu Ma, Bo Li, Hai Jin
Venue: KDD
Year: 2025

Vertical federated learning (VFL) enables a privacy-preserving collaboration among various parties to train a global model by melding their geo-distributed data features. Communication has been recognized as the primary bottleneck that impairs training efficiency due to frequent cross-party statistics exchange over wide area network. Existing synchronous VFL works often suffer from excessive communication overhead, while asynchronous schemes may introduce significant model staleness, potentially eroding the learning accuracy. In this paper, we propose BS-VFL, an asynchronous VFL with bounded staleness, to pipeline local computation and statistics transmission, substantially reducing the communication overhead while ensuring favorable model performance. Specifically, all data parties will give precedence to local model updates before generating embeddings to curtail model staleness. By analyzing convergence error, we show that BS-VFL can achieve a comparable result to synchronous VFL. Then, we develop a general framework to derive the closed-form wall-clock time of BS-VFL, offering a measure of its runtime efficiency and highlighting a marked communication reduction. Utilizing this convergence and time analysis, we refine learning parameters to minimize the convergence error for optimizing BS-VFL performance without compromising training efficiency. Extensive experiments on real-world datasets validate the superiority of BS-VFL over leading-edge methods, evidencing a reduction in training duration by 48%-90% while preserving model accuracy.
Graph Foundation Models: Challenges, Methods, and Open Questions
Authors: Zehong Wang, Chuxu Zhang, Jundong Li, Nitesh V. Chawla, Yanfang Ye
Venue: KDD
Year: 2025

Foundation models have revolutionized machine learning by enabling general-purpose reasoning across diverse tasks and domains. These models, pretrained on large-scale data, demonstrate strong adaptability with minimal task-specific supervision, leading to breakthroughs in natural language processing and computer vision. Inspired by this paradigm, Graph Foundation Models (GFMs) have emerged to extend the benefits of foundation models to graph-structured data, which is pretrained on massive graphs and can be fast adapted to different downstream tasks. In this paper, we provide a comprehensive survey of the state-of-the-art techniques of graph foundation models. In particular, we (1) formally categorize the challenges in designing graph foundation models; (2) comprehensively review the existing and recent advances of graph foundation models; (3) extend the graph foundation models in real-world problems; and (4) elucidate open questions and future research directions. Our systematic review summarizes representative models, highlights key design principles, and provides comparative analyses. This survey introduces major topics within foundation models and offers a guide to a new frontier of graph learning. Our extended survey is available at https://arxiv.org/abs/2505.15116.
ToolSQL: A Tool-Assisted Agent for SQL Verification and Refinement
Authors: Zhongyuan Wang, Richong Zhang, Zhijie Nie, Jaein Kim
Venue: KDD
Year: 2025

Recent Text-to-SQL methods leverage large language models (LLMs) by incorporating feedback from the database management system. While these methods effectively address execution errors in SQL queries, they struggle with database mismatches--errors that do not trigger execution exceptions. Database mismatches include issues such as condition mismatches and stricter constraint mismatches, both of which are more prevalent in real-world scenarios. To address these challenges, we propose a tool-assisted agent framework for SQL verification and refinement, equipping the LLM-based agent with two specialized tools: a retriever and a detector, designed to diagnose and correct SQL queries with database mismatches. These tools enhance the capability of LLMs to handle real-world questions more effectively. We also introduce SpiderMismatch, a new dataset specifically constructed to reflect the condition mismatch problems encountered in real-world scenarios. Empirical studies demonstrate the effectiveness of our proposed model on Spider and Spider-Realistic datasets in few-shot settings and confirm that our model outperforms baseline methods on SpiderMismatch.
Explicit-Implicit Entity Alignment Method in Multi-modal Knowledge Graphs
Authors: Luyao Wang, Chunlai Zhou, Biao Qin
Venue: KDD
Year: 2025

Multi-modal entity alignment (MMEA) aims to find the equivalent entities between multi-modal knowledge graphs (MMKGs). Current MMEA methods follow the ''embed-fuse-compare'' paradigm and show decent performance improvements on several public datasets. However, this paradigm may fail to fully address inconsistencies across different modalities, resulting in instability and sub-optimal performance. In this paper, we propose a novel paradigm called ''embed-fuse-assign'', which transforms MMEA into a multi-modal assignment problem. First, we prove that multi-modal entity alignment can be turned into assignment problems and identify that obtaining correspondence scores is the crucial step. Then, we devise a novel Explicit and Implicit multi-modal Entity Alignment (EIEA) algorithm, which classifies modalities into explicit and implicit categories based on the need for joint neighborhood information, designs modality-specific embedding and correlation scoring mechanisms, and derives alignment results by integrating correspondences across all modalities. Finally, we introduce a conflict-aware soft pseudo-labeling method to further optimize semi-supervised learning for implicit modality. Extensive experiments have demonstrated that our proposed paradigm and algorithm achieve the state-of-the-art performance on five real-world MMEA datasets. The source code and datasets are released at https://github.com/Bubble-bubble77/EIEA.
Graph Triple Attention Networks: A Decoupled Perspective
Authors: Xiaotang Wang, Yun Zhu, Haizhou Shi, Yongchao Liu, Chuntao Hong
Venue: KDD
Year: 2025

Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases. However, these methods face two primary challenges: (1) multi-view chaos, which results from coupling multi-view information (positional, structural, attribute), thereby impeding flexible usage and the interpretability of the propagation process. (2) local-global chaos, which arises from coupling local message passing with global attention, leading to issues of overfitting and over-globalizing. To address these challenges, we propose a high-level decoupled perspective of GTs, breaking them down into three components and two interaction levels: positional attention, structural attention, and attribute attention, alongside local and global interaction. Based on this decoupled perspective, we design a decoupled graph triple attention network named DeGTA, which separately computes multi-view attentions and adaptively integrates multi-view local and global information. This approach offers three key advantages: enhanced interpretability, flexible design, and adaptive integration of local and global information. Through extensive experiments, DeGTA achieves state-of-the-art performance across various datasets and tasks, including node classification and graph classification. Comprehensive ablation studies demonstrate that decoupling is essential for improving performance and enhancing interpretability. Our code is available at: https://github.com/wangxiaotang0906/DeGTA
Efficient Algorithms for Relevant Quantities of Friedkin-Johnsen Opinion Dynamics Model
Authors: Gengyu Wang, Runze Zhang, Zhongzhi Zhang
Venue: KDD
Year: 2025

Online social networks have become an integral part of modern society, profoundly influencing how individuals form and exchange opinions across diverse domains ranging from politics to public health. The Friedkin-Johnsen model serves as a foundational framework for modeling opinion formation dynamics in such networks. In this paper, we address the computational task of efficiently determining the equilibrium opinion vector and associated metrics including polarization and disagreement, applicable to both directed and undirected social networks. We propose a deterministic local algorithm with relative error guarantees, scaling to networks exceeding ten million nodes. Further acceleration is achieved through integration with successive over-relaxation techniques, where a relaxation factor optimizes convergence rates. Extensive experiments on diverse real-world networks validate the practical effectiveness of our approaches, demonstrating significant improvements in computational efficiency and scalability compared to conventional methods.
Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Authors: Chao Wang, Yue Zheng, Yujing Zhang, Yan Feng, Zhe Wang, Xiaowei Shi, An You, Yu Chen
Venue: KDD
Year: 2025

In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only pointwise modeling solutions can be adopted, focusing solely on modeling the likelihood of clicks or conversions for items by users to learn user-item preferences, without the ability to capture the ranking information among different items directly. However, since user-side information is often much more abundant than item-side information, the model can quickly learn the differences in user intrinsic tendencies, which are independent of the items they are exposed to. This can cause these intrinsic tendencies to become a shortcut bias for the model, leading to insufficient mining of the most concerned user-item preferences. To solve this challenge, we introduce the Breaker model. Breaker integrates an auxiliary task of user representation clustering with a multi-tower structure for cluster-specific preference modeling. By clustering user representations, we ensure that users within each cluster exhibit similar characteristics, which increases the complexity of the pointwise recommendation task on the user side. This forces the multi-tower structure with cluster-driven parameter learning to better model user-item preferences, ultimately eliminating shortcut biases related to user intrinsic tendencies. In terms of training, we propose a delayed parameter update mechanism to enhance training stability and convergence, enabling end-to-end joint training of the auxiliary clustering and classification tasks. Both offline and online experiments demonstrate that our method surpasses the baselines. It has already been deployed and is actively serving tens of millions of users daily on Meituan, one of the most popular e-commerce platforms for services.
Graph Evidential Learning for Anomaly Detection
Authors: Chunyu Wei, Wenji Hu, Xingjia Hao, Yunhai Wang, Yueguo Chen, Bing Bai, Fei Wang
Venue: KDD
Year: 2025

Graph anomaly detection faces significant challenges due to the scarcity of reliable anomaly-labeled datasets, driving the development of unsupervised methods. Graph autoencoders (GAEs) have emerged as a dominant approach by reconstructing graph structures and node features while deriving anomaly scores from reconstruction errors. However, relying solely on reconstruction error for anomaly detection has limitations, as it increases the sensitivity to noise and overfitting. To address these issues, we propose Graph Evidential Learning (GEL), a probabilistic framework that redefines the reconstruction process through evidential learning. By modeling node features and graph topology using evidential distributions, GEL quantifies two types of uncertainty: graph uncertainty and reconstruction uncertainty, incorporating them into the anomaly scoring mechanism. Extensive experiments demonstrate that GEL achieves state-of-the-art performance while maintaining high robustness against noise and structural perturbations.
Robust Tree-based Learned Vector Index with Query-aware Repartitioning
Authors: Wenqing Wei, Defu Lian, Qingshuai Feng, Yongji Wu
Venue: KDD
Year: 2025

Approximate Vector Retrieval (AVR), which aims to efficiently retrieve the most similar items from a large dataset, is a fundamental task in a variety of applications such as information retrieval, recommender systems, and large language models. Advances in representation learning and multimodal neural models have enabled diverse data types (e.g., text, images, audio) to be embedded into a shared vector space, facilitating similarity-based retrieval in AVR. While single-modal AVR assumes query and database embeddings follow the same distribution (In-Distribution, ID), cross-modal AVR introduces a distribution shift, where query vectors (e.g., text) are Out-of-Distribution (OOD) relative to the database (e.g., images). This mismatch complicates retrieval and degrades accuracy, making it a key challenge in AVR. Existing methods typically focus on either ID or OOD queries but struggle to handle both within a unified framework. In this paper, we propose RObust Tree-based LE arned IndeX (ROTLEX), a novel and robust method for effectively handling both ID and OOD queries in AVR. ROTLEX employs a hierarchical space partitioning structure and a query-aware repartitioning method. It recursively partitions the space into a K-ary tree, where separate encoders generate query embeddings that interact with shared tree node embeddings for retrieving vectors similar to the query. The repartitioning method transforms query routing probabilities into vector assignments using the same neural network. With a balanced assignment strategy, ROTLEX dynamically updates the tree and repartitions database vectors, significantly improving OOD query performance. Extensive experiments on real-world datasets show that ROTLEX achieves state-of-the-art trade-offs between latency and accuracy on both CPU and GPU.
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
Authors: Hechuan Wen, Tong Chen, Guanhua Ye, Li Kheng Chai, Shazia Sadiq, Hongzhi Yin
Venue: KDD
Year: 2025

Causal effect estimation (CEE) provides a crucial tool for predicting the unobserved counterfactual outcome for an entity. As CEE relaxes the requirement for "perfect'' counterfactual samples (e.g., patients with identical attributes and only differ in treatments received) that are impractical to obtain and can instead operate on observational data, it is usually used in high-stake domains like medical treatment effect prediction. Nevertheless, in those high-stake domains, gathering a decently sized, fully labelled observational dataset remains challenging due to hurdles associated with costs, ethics, expertise and time needed, etc., of which medical treatment surveys are a typical example. Consequently, if the training dataset is small in scale, low generalization risks can hardly be achieved on any CEE algorithms. Unlike existing CEE methods that assume the constant availability of a dataset with abundant samples, in this paper, we study a more realistic CEE setting where the labelled data samples are scarce at the beginning, while more can be gradually acquired over the course of training -- assuredly under a limited budget considering their expensive nature. Then, the problem naturally comes down to actively selecting the best possible samples to be labelled, e.g., identifying the next subset of patients to conduct the treatment survey. However, acquiring quality data for reducing the CEE risk under limited labelling budgets remains under-explored until now. To fill the gap, we theoretically analyse the generalization risk from an intriguing perspective of progressively shrinking its upper bound, and develop a principled label acquisition pipeline exclusively for CEE tasks. With our analysis, we propose the Model Agnostic Causal Active Learning (MACAL) algorithm for batch-wise label acquisition, which aims to reduce both the CEE model's uncertainty and the post-acquisition distributional imbalance simultaneously at each acquisition step. Extensive experiments are conducted on three datasets, where a clear empirical performance gain from MACAL is observed over state-of-the-art active learning baselines. The implementation repository is open-sourced at: https://github.com/uqhwen2/MACAL.
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
Authors: Zhenyu Wen, Wanglei Feng, Di Wu, Haozhen Hu, Chang Xu, Bin Qian, Zhen Hong, Cong Wang, Shouling Ji
Venue: KDD
Year: 2025

Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightforward in-training and post-training pricing techniques, we study a more difficult problem of pre-training pricing without direct information from the learning process. We propose FLMarket that integrates a two-stage pricing mechanism with a security protocol to address the utility-privacy conflict. Through comprehensive experiments, we show that the client selection according to FLMarket can achieve more than 10% higher accuracy in subsequent FL training compared to state-of-the-art methods. In addition, it outperforms the in-training baseline with more than 2% accuracy increase and 3× run-time speedup.
Revisiting Graph Adversarial Attack: A Perspective of Budget Optimization
Authors: Xiangchao Wen, Zhen Liu, Yuxin You
Venue: KDD
Year: 2025

Graph adversarial attacks refer to a class of adversarial attack methods targeting graph-structured data, such as social networks, knowledge graphs, and molecular structures. The objective of these attacks is to introduce subtle yet carefully designed perturbations to graph data (e.g., nodes, edges, or features) to cause graph-based machine learning models, such as graph neural networks (GNNs), to produce incorrect predictions or classification results. The core idea behind graph adversarial attacks is to exploit the vulnerabilities of the model, maximizing the degradation of its performance with minimal perturbations. However, due to the incompleteness of attack strategies, graph adversarial attacks often exhibit redundancy, meaning that some perturbations are ineffective. These ineffective perturbations consume the attack budget and increase the likelihood of detection. To address this issue, this paper proposes Budget-Reduced Attack Filtering (BRAF in short) frameworks capable of eliminating ineffective perturbations produced by existing graph adversarial attack methods, thereby optimizing the attack budget. Extensive experiments validate the effectiveness and scalability of the proposed filtering frameworks. The code is available at https://github.com/Xiangchao-Wen/Filter-Graph-Attack.
The 4th Workshop on AI Agent for Information Retrieval: Generating and Ranking
Authors: Qingsong Wen, Yongfeng Zhang, Zhiwei Liu, Julian J. McAuley, Hua Wei, Linsey Pang, Wei Liu, Philip S. Yu
Venue: KDD
Year: 2025

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized in- formation retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers and practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research and emerging trends, and foster knowledge exchange and collaboration within the community.