OpenRaiser/Intern-Atlas huggingface dataset:
EBES: Easy Benchmarking for Event Sequences
Authors: Dmitry Osin, Igor Udovichenko, Egor Shvetsov, Viktor Moskvoretskii, Evgeny Burnaev
Venue: KDD
Year: 2025

Event Sequences (EvS ) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these sequences is crucial for various real-life applications, including healthcare, finance, and user interaction. Despite the popularity of the EvS classification task, there is currently no standardized benchmark or rigorous evaluation protocol. This lack of standardization makes it difficult to compare results across studies, which can result in unreliable conclusions and hinder progress in the field. To address this gap, we present EBES, a comprehensive benchmark for EvS classification with sequence-level targets. EBES features standardized evaluation scenarios and protocols, along with an open-source PyTorch library. Code is available at https://github.com/On-Point-RND/EBES. Preprocessed data is available at https://huggingface.co/datasets/On-Point-Rnd/ebes that implements 9 modern models. Additionally, it includes the largest collection of EvS datasets, featuring 10 curated datasets, including a novel synthetic dataset and real-world data with the largest publicly available banking dataset. The library offers user-friendly interfaces for integrating new methods and datasets. Our benchmarking results highlight the unique properties of EvS compared to other sequential data types, provide a performance ranking of modern models-with GRU-based models achieving the best results-and reveal the challenges associated with robust EvS learning. The goal of EBES is to facilitate reproducible research, expedite progress in the field, and increase the real-world impact of EvS classification techniques.
Weight-Constrained Simple Path Enumeration in Weighted Graph
Authors: Dian Ouyang, Dong Wen, Jianye Yang, Wentao Li, Xuemin Lin
Venue: KDD
Year: 2025

Path enumeration is a fundamental problem and has been extensively studied in the literature. Given two query vertices and a weight threshold, the problem aims to identify all simple paths with weight not exceeding the threshold. Existing studies on path enumeration include DFS-based solutions and join-based solutions, where the join-based solutions only work for unweighted graphs. In this paper, we are the first to propose a join-based framework for weighted graphs. By observing the characteristics of DFS, we design a series of novel data structures and operations based on the join-based framework. In this way, our final solution combines the advantages of both join and DFS. We conduct experiments on several real large graphs. For weighted graphs, our method is much more efficient than existing algorithms. For unweighted graphs, our method is still competitive compared with the state-of-the-art solution which only works for unweighted graphs.
An Instructible Chemist-AI Alignment Framework for Generating Quaternary Ammonium Compound Structures
Authors: Bo Pan, Shiva Ghaemi, Amanda J. Consylman, Zihao Zhao, Ashley Ann Petersen, Alice Wu, Gabriel Chang, Diana McDonough, Mark A. Forman, Elise L. Bezold, William M. Wuest, Kevin Minbiole, Liang Zhao, Amarda Shehu
Venue: KDD
Year: 2025

This paper presents a novel Chemist-AI Alignment framework for generating novel structures of quaternary ammonium compounds (QACs), a crucial class of antimicrobial agents. The framework uniquely integrates AI-driven small molecule generation with iterative feedback from chemist experts, leveraging both rapid assessments and comprehensive wet-lab validations to optimize for biological potency and synthetic feasibility. Central to the framework is a hierarchical generative model that captures the QAC hierarchical topology. Extensive experiments highlight the efficacy of the framework in identifying promising QAC candidates, many of which are synthesized and evaluated in the wet laboratory, underscoring the potential of synergistic AI and domain-expert feedback in accelerating the design of novel antimicrobial agents.
MAD: Multicriteria Anomaly Detection of Suspicious Financial Accounts from Billions of Cash Transactions
Authors: Giordano Paoletti, Flavio Giobergia, Danilo Giordano, Luca Cagliero, Silvia Ronchiadin, Dario Moncalvo, Marco Mellia, Elena Baralis
Venue: KDD
Year: 2025

This paper presents a real-world deployment case study on using unsupervised anomaly detection for Anti-Money Laundering (AML). Using more than 2 billion anonymized bank transactions that Intesa Sanpaolo, a primary Italian financial institution, registered over 8 months, we developed, tuned and deployed a machine learning pipeline in production. Experts from Intesa Sanpaolo validated the performance of our approach against the institution's traditional rule-based system and checked new real-world cases the system allowed them to identify. Besides increasing both precision and recall by a factor of 6 in the detection of high-risk cases, our pipeline raises 200+ additional alerts during the 8-month period, manually identified by branch managers, but missed by the rule-based system. More importantly, a manual inspection of 100 new unseen cases revealed 28 significant previously unreported cases. The pipeline, now fully deployed in Intesa Sanpaolo's Transaction Monitoring system, highlights the advantages of machine learning over traditional approaches typically adopted in this traditionally very conservative sector.
Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures
Authors: John Paparrizos, Paul Boniol, Qinghua Liu, Themis Palpanas
Venue: KDD
Year: 2025

Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. In this paper, we present a process-centric taxonomy for time-series anomaly detection methods, systematically categorizing traditional statistical approaches and contemporary machine learning techniques. Beyond this taxonomy, we conduct a meta-analysis of the existing literature to identify broad research trends. Given the absence of a one-size-fits-all anomaly detector, we also introduce emerging trends for time-series anomaly detection. Furthermore, we review commonly used evaluation measures and benchmarks, followed by an analysis of benchmark results to provide insights into the impact of different design choices on model performance. Through these contributions, we aim to provide a holistic perspective on time-series anomaly detection and highlight promising avenues for future investigation.
PuzzleTensor: A Method-Agnostic Data Transformation for Compact Tensor Factorization
Authors: Yong-chan Park, Kisoo Kim, U Kang
Venue: KDD
Year: 2025

How can we achieve compact tensor representations without sacrificing reconstruction accuracy? Tensor decomposition is a cornerstone of modern data mining and machine learning, enabling efficient representations of multi-dimensional data through fundamental algorithms such as CP, Tucker, and Tensor-Train decompositions. However, directly applying these methods to raw data often results in high target ranks, poor reconstruction accuracy, and computational inefficiencies, as the data may not naturally conform to the low-rank structures these methods assume. In this paper, we propose PuzzleTensor, a method-agnostic data transformation technique for compact tensor factorization. Given a data tensor, PuzzleTensor ''solves the puzzle'' by shifting each hyperslice of the tensor to achieve accurate decompositions with significantly lower target ranks. PuzzleTensor offers three key advantages: (1) it is independent of specific decomposition methods, making it seamlessly compatible with various algorithms, such as CP, Tucker, and Tensor-Train decompositions; (2) it works under weak data assumptions, showing robust performance across both sparse and dense data, regardless of the rank; (3) it is inherently explainable, allowing clear interpretation of its learnable parameters and layer-wise operations. Extensive experiments show that PuzzleTensor consistently outperforms direct tensor decomposition approaches by achieving lower reconstruction errors and reducing the required target rank, making it a versatile and practical tool for compact tensor factorization in real-world applications.
Beyond Input Attribution: A Hands-On Tutorial to Concept-Based Explainable AI and Mechanistic Interpretability
Authors: Eliana Pastor, Eleonora Poeta, André Panisson, Alan Perotti, Gabriele Ciravegna
Venue: KDD
Year: 2025

As deep learning systems become pervasive, the demand for trustworthy and transparent AI continues to grow. Traditional feature attribution methods, however, often lack robustness and alignment with human reasoning. This tutorial moves beyond feature attribution by introducing participants to two complementary interpretability paradigms: Concept-Based Explainable AI (C-XAI) and Mechanistic Interpretability. C-XAI provides explanations grounded in high-level, human-interpretable concepts, bridging the gap between model reasoning and human understanding. In parallel, mechanistic interpretability--a quickly emerging field--focuses on reverse-engineering neural networks to uncover and disentangle the internal mechanisms that give rise to human-understandable representations. Through interactive coding sessions and hands-on exercises, attendees will gain practical experience implementing, evaluating, and comparing a variety of C-XAI and mechanistic interpretability techniques. By the end of the tutorial, participants will be equipped with a modern interpretability toolbox and a deeper understanding of how to apply them in real-world scenarios.
CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions
Authors: Yangzhe Peng, Kaiyuan Gao, Liang He, Yuheng Cong, Haiguang Liu, Kun He, Lijun Wu
Venue: KDD
Year: 2025

Molecular docking plays a crucial role in predicting the binding mode of ligands to target proteins, and covalent interactions, which involve the formation of a covalent bond between the ligand and the target, are particularly valuable due to their strong, enduring binding nature. However, most existing docking methods and deep learning approaches hardly account for the formation of covalent bonds and the associated structural changes. To address this gap, we introduce a comprehensive benchmark for covalent docking, CovDocker, which is designed to better capture the complexities of covalent binding. We decompose the covalent docking process into three main tasks: reactive location prediction, covalent reaction prediction, and covalent docking. By adapting state-of-the-art models, such as Uni-Mol and Chemformer, we establish baseline performances and demonstrate the effectiveness of the benchmark in accurately predicting interaction sites and modeling the molecular transformations involved in covalent binding. These results confirm the role of the benchmark as a rigorous framework for advancing research in covalent drug design. It underscores the potential of data-driven approaches to accelerate the discovery of selective covalent inhibitors and addresses critical challenges in therapeutic development.
Semantics-Aware Patch Encoding and Hierarchical Dependency Modeling for Long-Term Time Series Forecasting
Authors: Sijia Peng, Yun Xiong, Yangyong Zhu, Zhiqiang Shen
Venue: KDD
Year: 2025

Time series forecasting is a vital task with widespread applications. While recent advancements have adopted patching to enrich short-term context, existing encoding methods often struggle to capture the diverse semantics within patches, resulting in semantic information loss and limited model performance. Moreover, most long-term dynamics modeling approaches rely on homogeneous architectures with fixed receptive fields, inevitably sacrificing either performance or efficiency. To address these challenges, we propose Mixture of Universals (MoU), a novel framework designed to prevent semantic loss during patch encoding and efficiently enhance long-term dynamics through a hybrid approach. Specifically, MoU is consist of two novel designs: Mixture of Feature Extractors (MoF) and Mixture of Architectures (MoA). MoF introduces a semantics-aware encoding mechanism that selectively activates the corresponding sub-extractor based on the semantic context of input patches, preserving diverse temporal patterns and mitigating information loss. MoA, on the other hand, hierarchically captures long-term dependency with progressively expanded receptive field, improving model performance while maintaining relatively low computational costs. We conducted extensive experiments on seven real-world datasets, and the results demonstrate the superiority of our model. Our Code is available at https://github.com/lunaaa95/mou/.
Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Authors: Yifei Peng, Zijie Zha, Yu Jin, Zhexu Luo, Wang-Zhou Dai, Zhong Ren, Yao-Xiang Ding, Kun Zhou
Venue: KDD
Year: 2025

Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abductive visual Generation (AbdGen) approach to build such logic-integrated models. A vector-quantized symbol grounding mechanism and the corresponding disentanglement training method are introduced to enhance the controllability of logical symbols over generation. Furthermore, we propose two logical abduction methods to make our approach require few labeled training data and support the induction of latent logical generative rules from data. We experimentally show that our approach can be utilized to integrate various neural generative models with logical reasoning systems, by both learning from scratch or utilizing pre-trained models directly. The code is released at https://github.com/future-item/AbdGen.
NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
Authors: Adrien Petralia, Philippe Charpentier, Youssef Kadhi, Themis Palpanas
Venue: KDD
Year: 2025

Millions of smart meters have been deployed worldwide, collecting the total power consumed by individual households. Based on these data, electricity suppliers offer their clients energy monitoring solutions to provide feedback on the consumption of their individual appliances. Historically, such estimates have relied on statistical methods that use coarse-grained total monthly consumption and static customer data, such as appliance ownership. Non-Intrusive Load Monitoring (NILM) is the problem of disaggregating a household's collected total power consumption to retrieve the consumed power for individual appliances. Current state-of-the-art (SotA) solutions for NILM are based on deep-learning (DL) and operate on subsequences of an entire household consumption reading. However, the non-stationary nature of real-world smart meter data leads to a drift in the data distribution within each segmented window, which significantly affects model performance. This paper introduces NILMFormer, a Transformer-based architecture that incorporates a new subsequence stationarization/de-stationarization scheme to mitigate the distribution drift and that uses a novel positional encoding that relies only on the subsequence's timestamp information. Experiments with 4 real-world datasets show that NILMFormer significantly outperforms the SotA approaches. Our solution has been deployed as the backbone algorithm for EDF's (Electricité De France) consumption monitoring service, delivering detailed insights to millions of customers about their individual appliances' power consumption.
Alpine: Partial Unlabeled Graph Alignment
Authors: Petros Petsinis, Konstantinos Skitsas, Sayan Ranu, Davide Mottin, Panagiotis Karras
Venue: KDD
Year: 2025

Several applications call to align the nodes of two graphs in a way that minimizes a distance function. In practicality, the graphs to be aligned often have unequal orders (i.e., numbers of vertices) and no auxiliary labels or attributes; we refer to this problem as partial unlabeled graph alignment. Some proposals to address this problem add dummy nodes to the smaller graph to even the orders and align the ensuing graphs or employ embeddings such as GNNs, which yield ad hoc node representations. Unfortunately, as we show, an optimal solution to equal-order graph alignment using dummy nodes does not imply an optimal solution to partial graph alignment. To address this deficiency, in this paper, we propose Alpine, a Partial Unlabeled Graph Alignment algorithm that solely peruses the graphs' adjacency matrices, guided by a tailored objective function inspired by best-of-breed shape matching techniques and a state-of-the-art optimization method. Extensive experiments demonstrate that Alpine consistently surpasses state-of-the-art graph alignment methods in solution quality across all benchmark datasets.
Capillary Dataset: A Dataset of Nail-fold Capillaries Captured by Microscopy for Diabetes Detection
Authors: Hang Nguyen Thi Phuong, Hieyong Jeong
Venue: KDD
Year: 2025

Diabetes mellitus is a chronic condition marked by insufficient insulin utilization or production, causing metabolic dysregulation. If not controlled, it can cause serious complications that affect major organ systems such as the cardiovascular and ocular systems. Early diagnosis is essential for timely interventions to ensure effective glycemic control and lower the risk of complications. The present study introduced a robust and comprehensive dataset derived from nail-fold capillaroscopy. This dataset, which includes imaging and some video data from 126 individuals diagnosed with type 2 diabetes mellitus (T2DM) alongside 76 healthy controls, consisted of 3283 images obtained from diabetic subjects and 3412 images from non-diabetic participants. All images were acquired at 390× high magnification with a high resolution of 640×480 pixels, including corresponding video data, ensuring a thorough and detailed dataset for our study. The dataset was organized into two analytical tracks. The first track focused on nail-fold morphology, classifying images into four types: hairpin, crossing, tortuous, and bushy. After filtering out duplicates and low-quality images, 1279 images were selected for analysis. The second track involved binary classification for the detection of diabetes, differentiating healthy individuals from people with diabetes. This utilized the complete dataset and the augmented versions created by combining multiple images into composite formats to enhance feature representation. Additionally, we evaluated several benchmark deep-learning models such as Vision Transformers (ViT) and Convolutional Neural Networks (CNNs) for morphological classification and diabetes detection tasks. This analysis illuminated current model performance, highlighted challenges, and paved the way for future research opportunities. Importantly, this dataset is critical and holds significant potential for advancing non-invasive automated diagnostic methodologies in diabetes-related nail-fold capillary research, offering a promising future for diabetes research. The dataset is available at: https://huggingface.co/datasets/HanaNguyen/Capillary-Dataset. The github: https://github.com/urgonguyen/Capillarydataset.git.
KDD Health Day 2025: Harnessing AI Opportunities in Biomedicine and Healthcare
Authors: Peipei Ping, Wei Ding, Carl Yang
Venue: KDD
Year: 2025

The ACM KDD 2025 Health Day theme, ''Harnessing AI Opportunities in Biomedicine and Healthcare'' highlights the transformative potential of AI-driven applications in healthcare, translational biomedical research, and basic biological research. This extended abstract discusses recent advancements, challenges, and future directions, focusing on integrating AI-ready data sets, interdisciplinary collaborations, and ethical AI practices. It aims to catalyze discussions on the potential of AI ecosystems in revolutionizing healthcare and related fields.
On the Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification
Authors: Jatin Prakash, Anirudh Buvanesh, Bishal Santra, Deepak Saini, Sachin Yadav, Jian Jiao, Yashoteja Prabhu, Amit Sharma, Manik Varma
Venue: KDD
Year: 2025

Extreme Classification (XC) aims to map a query to the most relevant documents from a very large document set. XC algorithms used in real-world applications typically learn this mapping from datasets curated from implicit feedback, such as user clicks. However, these datasets often suffer from missing labels. In this work, we observe that systematic missing labels lead to missing knowledge, which is critical for modelling relevance between queries and documents. We formally show that this absence of knowledge is hard to recover using existing methods such as propensity weighting and data imputation strategies that solely rely on the training dataset. While Large Language Models (LLMs) provide an attractive solution to augment the missing knowledge, leveraging them in applications with low latency requirements and large document sets is challenging. To mitigate missing knowledge at scale, we propose SKIM (Scalable Knowledge Infusion for Missing Labels), an algorithm that leverages a combination of Small Language Models or SLMs, e.g., Llama2-7b, and abundant unstructured meta-data to effectively address the missing label problem. We show the efficacy of our method on large-scale public datasets through a combination of unbiased evaluation strategies, such as exhaustive human annotations and simulation-based evaluation benchmarks. SKIM outperforms existing methods on Recall@100 by more than 10 absolute points. Additionally, SKIM scales to proprietary query-ad retrieval datasets containing 10 million documents, outperforming baseline methods by 12% in offline evaluations and increasing ad click-yield by 1.23% in an online A/B test conducted on Bing Search. We release the code and trained models at: github.com/bicycleman15/skim
Understanding the Effect of Loss Functions on the Generalization of Recommendations
Authors: Yuanhao Pu, Defu Lian, Xiaolong Chen, Jin Chen, Ze Liu, Enhong Chen
Venue: KDD
Year: 2025

The two-tower model has become prevalent in recommender systems for its computational efficiency and robust predictive capabilities. The model usually employs two independent neural networks to encode user and item data separately, and predicts the similarity score with inner product or cosine functions, depending on which the Top-k ranked item list is generated. The optimization process typically involves a multi-label classification objective, often guided by surrogate loss functions like Softmax and One-vs-All (OvA), to enhance the recommendation performance. Despite both Softmax and OvA losses being Bayes-consistent, empirical observations reveal a significant performance gap in evaluation metrics, suggesting limitations in Bayes-consistency for analyzing loss effectiveness. To address this, we introduce ℋ-consistency into the discussion, which provides non-asymptotic and hypothesis-specific guarantees for Top-k classification within the two-tower model's hypothesis space. Through theoretical analysis, we demonstrate that Softmax and Cosine Contrastive Loss exhibit ℋ-consistency, while the OvA loss does not, explaining the observed performance discrepancies. Our findings bridge the gap between theoretical properties and practical outcomes, offering deeper insights into the optimization of two-tower models and contributing to the development of more effective recommendation systems.