OpenRaiser/Intern-Atlas huggingface dataset:
RegCheck: A Real-Time Approach for Flagging Potentially Malicious Domain Name Registrations
Authors: Thomas Daniels, Maarten Bosteels, Pieter Robberechts, Jesse Davis
Venue: KDD
Year: 2025

Miscreants use domain names for malicious purposes such as phishing websites or fake webshops. Reactive approaches such as blocklists play an important role in fighting such abuse but have limitations, namely that the domains are typically only included in such a list after abuse has been reported (e.g., there may already be some victims). We propose RegCheck, a system designed to proactively flag suspicious domains at registration time. The core of RegCheck is a machine learning classifier that assesses the risk that the domain name will be used for malicious purposes based on characteristics known at the time of registration. Based on this assessment, it flags some registrations and requires them to undergo additional verification prior to the domain name being activated. The system has been developed collaboratively between SIDN (.nl) and DNS Belgium (.be) and has been deployed as a real-time system at the .be registry since March 2024. Since its deployment, the registry has witnessed a decrease in the number of .be domain name registrations that have been revoked for breaching the terms and conditions, indicating a decline in the number of active malicious registrations.
CODA: Temporal Domain Generalization via Concept Drift Simulator
Authors: Chia-Yuan Chang, Yu-Neng Chuang, Zhimeng Jiang, Kwei-Herng Lai, Anxiao Jiang, Na Zou
Venue: KDD
Year: 2025

Machine learning models in real-world applications often suffer performance issues due to data distribution shifts. Temporal domain generalization aims to adapt models to the ''concept drift,'' maintaining future performance. Existing works based on model-centric training strategies may entail extensive interaction between data and model to appropriately train the model for distribution shifts. To this end, we aim to nip the problem in the bud by generating future domain data for model training and naturally bypassing the cumbersome interaction between data and model. We propose the COncept Drift simulAtor (CODA) framework incorporating a predicted feature correlation matrix to simulate future data for model training. Specifically, the feature correlations matrix serves as a delegation to represent data characteristics at each time point and the trigger for future data generation. Experimental results demonstrate that using CODA-generated data as training input effectively achieves temporal domain generalization across different model architectures with great transferability.
FedDiAL: Adaptive Federated Learning with Hierarchical Discriminative Network for Large Pre-trained Models
Authors: Gang Yan, Wan Du
Venue: KDD
Year: 2025

Large pre-trained models have significantly advanced computer vision (CV) and natural language processing (NLP). However, their deployment is challenging in privacy-sensitive scenarios where data must remain decentralized. Federated Learning (FL) addresses this issue by enabling local model training without sharing raw data. Despite this advantage, integrating large models into FL introduces challenges such as training inefficiencies, label scarcity, and data heterogeneity. In this paper, we propose FedDiAL, a framework designed to effectively incorporate large pre-trained models into FL while addressing these challenges. At its core, FedDiAL, features the novel HDis-Net, which enhances the efficiency of large models in resource-constrained environments. We introduce a two-phase training strategy with probabilistic feature augmentation to improve feature discrimination. Additionally, we propose an adaptive pseudo-labeling method to generate high-confidence labels, mitigating label scarcity. To handle data heterogeneity, we develop a focused fine-tuning strategy that adapts HDis-Net to diverse client data distributions. Our theoretical analysis establishes the convergence of HDis-Net. Extensive experiments on four datasets, including Tiny ImageNet and AG News, demonstrate that FedDiAL outperforms state-of-the-art methods, achieving up to a 35.66% accuracy improvement in both CV and NLP tasks.
PMKLC: Parallel Multi-Knowledge Learning-based Lossless Compression for Large-Scale Genomics Database
Authors: Hui Sun, Yanfeng Ding, Liping Yi, Huidong Ma, Gang Wang, Xiaoguang Liu, Cheng Zhong, Wentong Cai
Venue: KDD
Year: 2025

Learning-based lossless compressors play a crucial role in large-scale genomic database backup, storage, transmission, and management. However, their 1) inadequate compression ratio, 2) low compression & decompression throughput, and 3) poor compression robustness limit their widespread adoption and application in both industry and academia. To solve those challenges, we propose a novel Parallel Multi-Knowledge Learning-based Compressor (PMKLC) with four crucial designs: 1) We propose an automated multi-knowledge learning-based compression framework as compressors' backbone to enhance compression ratio and robustness; 2) we design a GPU-accelerated (s,k)-mer encoder to optimize compression throughput and computing resource usage; 3) we introduce data block partitioning and Step-wise Model Passing (SMP) mechanisms for parallel acceleration; 4) We design two compression modes PMKLC-S and PMKLC-M to meet the complex application scenarios, where the former runs on a resource-constrained single GPU and the latter is multi-GPU accelerated. We benchmark PMKLC-S/M and 14 baselines (7 traditional and 7 leaning-based) on 15 real-world datasets with different species and data sizes. Compared to baselines on the testing datasets, PMKLC-S/M achieve the average compression ratio improvement up to 73.609% and 73.480%, the average throughput improvement up to 3.036X and 10.710X, respectively. Besides, PMKLC-S/M also achieve the best robustness and competitive memory cost, indicating its greater stability against datasets with different probability distribution perturbations, and its strong ability to run on memory-constrained devices. Overall, PMKLC is a balanced compression solution that optimizes compression ratio, throughput, robustness, and resource consumption. PMKLC and linkages of datasets are available at https://github.com/dingyanfeng/PMKLC.
FlexGNN: A High-Performance, Large-Scale Full-Graph GNN System with Best-Effort Training Plan Optimization
Authors: Jeongmin Bae, Donghyoung Han, Min-Soo Kim
Venue: KDD
Year: 2025

Recently, full-graph Graph Neural Networks (GNNs) have gained prominence by addressing complex problems such as weather forecasting and material discovery. Existing full-graph training methods do not fully manage intermediate data generated during training and rely on rigid inter-GPU communication, limiting both training speed and scale. We propose FlexGNN, which fully manages intermediate data and adaptively performs inter-GPU communication by generating and optimizing best-effort training execution plans. Extensive experiments demonstrate that FlexGNN significantly outperforms existing full-graph GNN methods in both training speed and scale. Specifically, it is up to 5.4X faster than HongTu and up to 95.5X faster than NeutronStar.
CoopRide: Cooperate All Grids in City-Scale Ride-Hailing Dispatching with Multi-Agent Reinforcement Learning
Authors: Jingwei Wang, Qianyue Hao, Wenzhen Huang, Xiaochen Fan, Qin Zhang, Zhentao Tang, Bin Wang, Jianye Hao, Yong Li
Venue: KDD
Year: 2025

Ride-hailing services offer convenient travel options in urban transportation. To improve passengers' experience and platforms' revenue, plentiful studies use multi-agent reinforcement learning (MARL) for efficient order dispatching, controlling each grid with one agent to balance the supply-demand (drivers-orders) distribution. However, despite the critical role of cooperation among grids for efficient dispatching strategies, existing works neglect it or limit it within neighboring grids. There exist three key challenges in scaling the cooperation to the whole city: (1) cooperative strategies cause complex interactions among grids, making the grids' states coupled and complicating the information extraction from the states for decision-making; (2) cooperation among grids requires both within- and cross-grid dispatching, where the priorities of these two types of actions are difficult to balance; (3) the value of cooperation is not only heterogeneous over different pairs of grids, but also varies temporally, adding difficulty to dynamically determine the intensities of cooperation for each pair of grids and obtain the global cooperation rewards. In this paper, we propose the CoopRide framework to solve the above challenges. We model the interactions among agents with graphs and utilize graph neural network (GNN) for efficient information extraction. We uniformly encode both within- and cross-grid dispatching, enabling flexible choice of both types of actions in the embedding space. We also design to automatically learn the cooperation intensities among grids, thereby obtaining the cooperative rewards to drive the learning of global cooperation actions. We conduct experiments in three real-world datasets with millions of orders, and extensive results demonstrate the superior performance of CoopRide, outperforming the state-of-the-art baselines by up to 12.4%. Our source codes are available at https://github.com/tsinghua-fib-lab/CoopRide.
A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare
Authors: Ran Xu, Patrick Jiang, Linhao Luo, Cao Xiao, Adam Cross, Shirui Pan, Jimeng Sun, Carl Yang
Venue: KDD
Year: 2025

In recent years, the landscape of digital biomedicine and healthcare has been reshaped due to the disruptive breakthroughs in AI-facilitated by tremendous data and high-performance computers, large language models (LLMs) have transformed information technology from accessing data to performing analytical tasks. While demonstrating unprecedented capabilities, LLMs have been found unreliable in tasks requiring factual knowledge and rigorous reasoning. Biomedicine and healthcare, as an important vertical domain rapidly benefitting from progress in AI, necessitates strict requirements on the accuracy, controllability, and interpretability of analytical models, posing critical challenges for LLMs. Despite recent studies addressing the hallucination problem of LLMs, research on empowering LLMs with the ability to plan, reason, and ground with explicit knowledge has also started to prosper, especially in the biomedicine and healthcare domain. On the other hand, biomedical data are enormous and notoriously complex, coming from various sources (e.g., biomedical knowledge bases, online literature, and hospitals) and bearing various modalities (e.g., tables, texts, images and time-series). Healthcare professionals have spent decades collecting, cleaning, and curating various types of data. The processes are extremely costly, producing various datasets with different data schemas, coding systems, and quality standards, many privately owned by the creators, making their integrative analysis and utilization through unified AI techniques still rather challenging. The generalizability of LLMs across different types of data endow them strong promises in automating the processing of large-scale complex healthcare data such as into unified knowledge graphs (KGs). Our goal in this survey is to systematically investigate and summarize recent studies on the unification of LLMs and KGs, towards fully utilizing the value of complex data, unleashing the power of generative AI, and expediting next-generation AI for biomedicine and healthcare applications.
Measuring Item Freshness in Data Streams
Authors: Zirui Liu, Zihan Jiang, An Zhang, Zhouran Shi, Yuxuan Tian, Tong Yang
Venue: KDD
Year: 2025

This paper studies an unexplored attribute in data streams - item freshness. The freshness of an item refers to the time interval between its last arrival and the present moment. The information of item freshness is useful in various scenarios like cache, online advertising, computer network, etc. Currently, there is no algorithm tailored for estimating item freshness. We propose a theoretically guaranteed sketch algorithm called RingSketch, which integrates time-agnostic sketch algorithm with time-aware CLOCK algorithm for real-time freshness measurement. With the key idea of tracing the trajectory of the clock pointer, the estimation process of RingSketch is akin to observing the length of the growth rings in a tree trunk. We theoretically derive the average error of RingSketch and validate it with extensive experiments. The results show that RingSketch simultaneously achieves high accuracy (<10-3 average relative error) and fast update speed (>11.4 M/s), outperforming the baseline solutions by at least 13.3x and 1.5x respectively. All codes are open-sourced at GitHub.
Bias Detection via Maximum Subgroup Discrepancy
Authors: Jiri Nemecek, Mark Kozdoba, Illia Kryvoviaz, Tomás Pevný, Jakub Marecek
Venue: KDD
Year: 2025

Bias evaluation is fundamental to trustworthy AI, both in terms of checking data quality and in terms of checking the outputs of AI systems. In testing data quality, for example, one may study the distance of a given dataset, viewed as a distribution, to a given ground-truth reference dataset. However, classical metrics, such as the Total Variation and the Wasserstein distances, are known to have high sample complexities and, therefore, may fail to provide a meaningful distinction in many practical scenarios. In this paper, we propose a new notion of distance, the Maximum Subgroup Discrepancy (MSD). In this metric, two distributions are close if, roughly, discrepancies are low for all feature subgroups. While the number of subgroups may be exponential, we show that the sample complexity is linear in the number of features, thus making it feasible for practical applications. Moreover, we provide a practical algorithm for evaluating the distance based on Mixed-integer optimization (MIO). We also note that the proposed distance is easily interpretable, thus providing clearer paths to fixing the biases once they have been identified. Finally, we describe a natural general bias detection framework, termed MSDD distances, and show that MSD aligns well with this framework. We empirically evaluate MSD by comparing it with other metrics and by demonstrating the above properties of MSD on real-world datasets.
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
Venue: KDD
Year: 2025

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 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.
Addressing and Visualizing Misalignments in Human Task-Solving Trajectories
Authors: Sejin Kim, Hosung Lee, Sundong Kim
Venue: KDD
Year: 2025

Understanding misalignments in human task-solving trajectories is crucial for enhancing AI models trained to closely mimic human reasoning. This study categorizes such misalignments into three types: (1) Lack of functions to express intent(2) inefficient action sequences, and (3) incorrect intentions that cannot solve the task. To address these issues, we first formalize and define these three misalignment types in a unified framework. We then propose a heuristic algorithm to detect misalignments in ARCTraj trajectories and analyze their impact hierarchically and quantitatively. We also present an intention estimation method based on our formalism that infers missing alignment between user actions and intentions. Through trajectory alignment, we experimentally demonstrate that AI models trained on human task-solving trajectories improve performance in mimicking human reasoning. Based on hierarchical analysis and experiments, we highlight the importance of trajectory-intention alignment and demonstrate the effectiveness of intention-aligned training.
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
Authors: Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo, Hua Wei
Venue: KDD
Year: 2025

Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models better and extract valuable insights from their predictions. While numerous post-hoc instance-level explanation methods have been proposed to interpret GNN predictions, the reliability of these explanations remains uncertain, particularly in the out-of-distribution or unknown test datasets. In this paper, we address this challenge by introducing an explainer framework with the confidence scoring module ( ConfExplainer), grounded in theoretical principle, which is generalized graph information bottleneck with confidence constraint (GIB-CC), that quantifies the reliability of generated explanations. Experimental results demonstrate the superiority of our approach, highlighting the effectiveness of the confidence score in enhancing the trustworthiness and robustness of GNN explanations.
MOSS: Multi-Objective Optimization for Stable Rule Sets
Authors: Brian Liu, Rahul Mazumder
Venue: KDD
Year: 2025

We present MOSS, a multi-objective optimization framework for constructing stable sets of decision rules. MOSS incorporates three important criteria for interpretability: sparsity, accuracy, and stability, into a single multi-objective optimization framework. Importantly, MOSS allows a practitioner to rapidly evaluate the trade-off between accuracy and stability in sparse rule sets in order to select an appropriate model. We develop a specialized cutting plane algorithm in our framework to rapidly compute the Pareto frontier between these two objectives, and our algorithm scales to problem instances beyond the capabilities of commercial optimization solvers. Our experiments show that MOSS outperforms state-of-the-art rule ensembles in terms of both predictive performance and stability.
Paper-Level Computerized Adaptive Testing for High-Stakes Examination via Multi-Objective Optimization
Authors: Mingjia Li, Junkai Tong, Yiyang Huang, Yifei Ding, Hong Qian, Aimin Zhou
Venue: KDD
Year: 2025

Computerized Adaptive Testing (CAT) is a testing technique that accurately infers students' proficiency levels using a relatively small number of questions. Most existing CAT systems operate on a question-level adaptive paradigm, which is suitable for practice scenarios. However, in computerized standardized high-stakes examinations such as the GRE and GMAT, this paradigm faces several challenges: (1) the lack of comparability in exam results(2) high implementation costs due to the reliance on real-time interactions and the financial burden of maintaining CAT testing system, and (3) the difficulty in balancing multiple factors of diagnosis quality, attribute coverage, and question exposure. To address these challenges, we propose a Paper-level Computerized Adaptive Testing (PCAT) and its corresponding evaluation method. PCAT divides an exam into multiple testing stages, where examinees adaptively receive test papers of varying difficulty based on their performance in previous stages. The paper assembly problem in PCAT is solved using a population-based multi-objective optimization (MOO) approach. PCAT offers several advantages: First, the paper-level adaptive mechanism ensures that the questions faced by examinees depend solely on their performance in the earlier stages, maintaining adaptability while enhancing the comparability of results across different examinees. Second, PCAT replaces the selection strategy module in traditional CAT with an assembly module, allowing computationally intensive tasks such as cognitive diagnosis and paper assembly to be completed offline before the exam, eliminating the need for real-time interactions. Additionally, the population-based MOO approach generates a set of high-quality solutions in one run, meeting the demands of frequent administration of standardized high-stakes exams like the GRE and reducing the financial burden of maintaining a large-scale CAT system. Finally, MOO naturally models multiple factors as separate objectives, enabling a balanced consideration of these factors and allowing exam administrators to customize the exam based on specific needs. Extensive experiments on four real-world datasets show that PCAT outperforms state-of-the-art (SOTA) CAT methods in terms of diagnosis quality, attribute coverage, and question exposure, while maintaining the same number of questions answered by examinees. These results highlight PCAT's potential in high-stakes examination settings.
Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness
Authors: Chuang Zhao, Hui Tang, Hongke Zhao, Xiaomeng Li
Venue: KDD
Year: 2025

Advanced healthcare predictions offer significant improvements in patient outcomes by leveraging predictive analytics. Existing works primarily utilize various views of Electronic Health Record (EHR) data, such as diagnoses, lab tests, or clinical notes, for model training. These methods typically assume the availability of complete EHR views and that the designed model could fully leverage the potential of each view. However, in practice, random missing views and view laziness present two significant challenges that hinder further improvements in multi-view utilization. To address these challenges, we introduce Diffmv, an innovative diffusion-based generative framework designed to advance the exploitation of multiple views of EHR data. Specifically, to address random missing views, we integrate various views of EHR data into a unified diffusion-denoising framework, enriched with diverse contextual conditions to facilitate progressive alignment and view transformation. To mitigate view laziness, we propose a novel reweighting strategy that assesses the relative advantages of each view, promoting a balanced utilization of various data views within the model. Our proposed strategy achieves superior performance across multiple health prediction tasks derived from three popular datasets, including multi-view and multi-modality scenarios.