OpenRaiser/Intern-Atlas huggingface dataset:
From Expansion to Retraction: Long-tailed Machine Unlearning via Boundary Manipulation
Authors: Min Chen, Weizhuo Gao, Chen Wang, Gaoyang Liu, Ahmed M. Abdelmoniem, Kai Peng
Venue: KDD
Year: 2025

Machine unlearning aims to remove the information of specific data from a trained machine learning model while retaining its utility for the remaining data, so as to meet the requirements of privacy regulations. Existing unlearning methods often assume a balanced data distribution, but neglect the real-world, long-tailed scenarios, where the decision boundaries of tail classes are frequently distorted due to insufficient sample representation, thereby reducing the unlearning efficacy. In this paper, we propose the first Long-Tailed Machine Unlearning (LTMU) framework from a unified decision-boundary perspective. Our framework begins with a directional boundary repair scheme designed to enrich the distorted decision boundary of the tail class, and then develop a novel boundary retraction approach tailored for long-tailed unlearning, dispersing both the augmented and original features throughout the feature space. This bidirectional manipulation not only offers a unified interpretation of the relationship between long-tailed learning and unlearning, but also enables flexible control over both repair and unlearning processes through the generation of augmented features, thereby effectively accomplishing the long-tailed unlearning task. Extensive experiments across multiple datasets and neural network architectures demonstrate the effectiveness of our framework in achieving complete unlearning of tail classes in long-tailed distributions.
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
Authors: Kaixuan Chen, Wei Luo, Shunyu Liu, Yaoquan Wei, Yihe Zhou, Yunpeng Qing, Quan Zhang, Yong Wang, Jie Song, Mingli Song
Venue: KDD
Year: 2025

In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, our proposed approach, named Powerformer, develops a dedicated section-adaptive attention mechanism, separating itself from the self-attention employed in conventional transformers. This mechanism effectively integrates power system states with transmission section information, which facilitates the development of robust state representations. Furthermore, by considering the graph topology of power system and the electrical attributes of bus nodes, we introduce two customized strategies to further enhance the expressiveness: graph neural network propagation and multi-factor attention mechanism. Extensive evaluations are conducted on three power system scenarios, including the IEEE 118-bus system, a realistic China 300-bus system, and a large-scale European system with 9241 buses, where Powerformer demonstrates its superior performance over several popular baseline methods. The code is available at: https://github.com/Cra2yDavid/Powerformer
Advancing Confidence Calibration and Quantification in Medication Recommendation
Authors: Qianyu Chen, Xin Li, Yujie Fang, Mingzhong Wang
Venue: KDD
Year: 2025

Medication recommendation (MR) has undergone rapid advancement in recent years, driven by its significant practical implications in healthcare. However, such high-risk scenarios still experience two critical yet overlooked challenges: the prevalent overconfidence in raw confidence for individual medications and the lack of a robust solution for confidence quantification in medication combinations. This paper represents the first in-depth study addressing this gap. We introduce two innovative methodologies tailored to the unique challenges of MR scenarios: 1) A discernible binning-based calibration method with theoretical guarantees for the confidence of individual medication. It guarantees distinct accuracy levels between adjacent bins and maintains consistent statistical reliability across calibration and test data, enabling calibrated confidence to reflect the correctness of medication recommendations distinctively. 2) A sample-based quantification method for the set confidence of medication combination, which is applicable for various existing performance metrics in MR. Utilizing representative deep MR models as backbones and conducting extensive experiments on the widely recognized MIMIC datasets, we empirically prove the effectiveness and robustness of our proposed methods. Our approaches not only improve the reliability of MR but also pave the way for more informed decision-making in clinical settings.
NodeImport: Imbalanced Node Classification with Node Importance Assessment
Authors: Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Jun Hu, Jia Chen
Venue: KDD
Year: 2025

In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performance. Traditional Graph Neural Networks (GNNs) often struggle in such scenarios, as they tend to overfit to majority classes while underrepresenting minority classes. Existing solutions, which either prioritize nodes based on class size or synthesize new nodes for minority classes, often fall short of effectively addressing this imbalance issue. This paper introduces a novel approach to class-imbalanced node classification by utilizing a balanced meta-set for importance measurement, where a training node is considered significant if it enhances model performance under an unbiased setting. Our method identifies important nodes that can counteract class imbalance and utilizes them for model training, allowing for fine-grained and dynamic node selection throughout the training process. We theoretically derive a formula to directly assess node importance, reducing computational overhead and providing an intuitive threshold for node selection. Guided by this metric, we develop a novel framework that filters valuable labeled, unlabeled, and synthetic nodes that enhance model performance in an unbiased context. A key advantage of this framework is its separation of the synthetic node generation process from the filtering process, ensuring compatibility with various node generation techniques. Furthermore, we introduce a strategy to construct a high-quality meta-set that closely approximates the overall feature distribution, ensuring robust representation of each class. We evaluate our framework, NodeImport, across multiple benchmark datasets using popular GNN architectures, demonstrating its superiority over state-of-the-art baselines. Our results highlight the flexibility and effectiveness of the framework in mitigating class imbalance, leading to improved node classification outcomes. The source code is available at https://github.com/NanChanNN/NodeImport.
ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Models
Authors: Haibin Chen, Kangtao Lv, Chengwei Hu, Yanshi Li, Yujin Yuan, Yancheng He, Xingyao Zhang, Langming Liu, Shilei Liu, Wenbo Su, Bo Zheng
Venue: KDD
Year: 2025

With the increasing use of Large Language Models (LLMs) in fields such as e-commerce, domain-specific concept evaluation benchmarks are crucial for assessing their domain capabilities. Existing LLMs may generate factually incorrect information within the complex e-commerce applications. Therefore, it is necessary to build an e-commerce concept benchmark. Existing benchmarks encounter two primary challenges: (1) handle the heterogeneous and diverse nature of tasks(2) distinguish between generality and specificity within the e-commerce field. To address these problems, we propose ChineseEcomQA, a scalable question-answering benchmark focused on fundamental e-commerce concepts. ChineseEcomQA is built on three core characteristics: Focus on Fundamental Concept, E-commerce Generality and E-commerce Expertise. Fundamental concepts are designed to be applicable across a diverse array of e-commerce tasks, thus addressing the challenge of heterogeneity and diversity. Additionally, by carefully balancing generality and specificity, ChineseEcomQA effectively differentiates between broad e-commerce concepts, allowing for precise validation of domain capabilities. We achieve this through a scalable benchmark construction process that combines LLM validation, Retrieval-Augmented Generation (RAG) validation, and rigorous manual annotation. Based on ChineseEcomQA, we conduct extensive evaluations on mainstream LLMs and provide some valuable insights. We hope that ChineseEcomQA could guide future domain-specific evaluations, and facilitate broader LLM adoption in e-commerce applications.
Mini-Game Lifetime Value Prediction in WeChat
Authors: Aochuan Chen, Yifan Niu, Ziqi Gao, Yujie Sun, Shoujun Liu, Gong Chen, Yang Liu, Jia Li
Venue: KDD
Year: 2025

The LifeTime Value (LTV) prediction, which endeavors to forecast the cumulative purchase contribution of a user to a particular item, remains a vital challenge that advertisers are keen to resolve. A precise LTV prediction system enhances the alignment of user interests with meticulously designed advertisements, thereby generating substantial profits for advertisers. Nonetheless, this issue is complicated by the paucity of data typically observed in real-world advertising scenarios. The purchase rate among registered users is often as critically low as 0.1%, resulting in a dataset where the majority of users make only several purchases. Consequently, there is insufficient supervisory signal for effectively training the LTV prediction model. An additional challenge emerges from the interdependencies among tasks with high correlation. It is a common practice to estimate a user's contribution to a game over a specified temporal interval. Varying the lengths of these intervals corresponds to distinct predictive tasks, which are highly correlated. For instance, predictions over a 7-day period are heavily reliant on forecasts made over a 3-day period, where exceptional cases can adversely affect the accuracy of both tasks. In order to comprehensively address the aforementioned challenges, we introduce an innovative framework denoted as Graph-Represented Pareto-Optimal LifeTime Value prediction (GRePO-LTV). Graph representation learning is initially employed to address the issue of data scarcity. Subsequently, Pareto-Optimization is utilized to manage the interdependence of prediction tasks. Our method is evaluated using a proprietary offline mini-game recommendation dataset in conjunction with an online A/B test. The implementation of our method results in a significant enhancement within the offline dataset. Moreover, the A/B test demonstrates encouraging outcomes, increasing average Gross Merchandise Value (GMV) by 8.4%.
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs
Authors: Jiawen Chen, Qi Shao, Duxin Chen, Wenwu Yu
Venue: KDD
Year: 2025

Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often struggle to balance model expressiveness and computational efficiency, especially when scaling to large real-world datasets. To tackle these challenges, we propose STH-SepNet (Spatio-Temporal Hypergraph Separation Networks), a novel framework that decouples temporal and spatial modeling to enhance both efficiency and precision. Therein, the temporal dimension is modeled using lightweight large language models, which effectively capture low-rank temporal dynamics. Concurrently, the spatial dimension is addressed through an adaptive hypergraph neural network, which dynamically constructs hyperedges to model intricate, higher-order interactions. A carefully designed gating mechanism is integrated to seamlessly fuse temporal and spatial representations. By leveraging the fundamental principles of low-rank temporal dynamics and spatial interactions, STH-SepNet offers a pragmatic and scalable solution for spatio-temporal prediction in real-world applications. Extensive experiments on large-scale real-world datasets across multiple benchmarks demonstrate the effectiveness of STH-SepNet in boosting predictive performance while maintaining computational efficiency. This work may provide a promising lightweight framework for spatio-temporal prediction, aiming to reduce computational demands and while enhancing predictive performance. Our code is avaliable at https://github.com/SEU-WENJIA/ST-SepNet-Lightweight-LLMs-Meet-Adaptive-Hypergraphs.
Scalable Link Recommendation for Influence Maximization
Authors: Xiaolong Chen, Jing Tang
Venue: KDD
Year: 2025

The rise of link recommendation systems in online social networks has sparked significant research interest in strategically adding links to enhance social influence. This paper delves into the influence maximization with augmentation (IMA) problem that aims to add k edges connecting seed nodes and ordinary nodes to boost the influence propagation of the given seed set. IMA is a monotone submodular maximization problem so that the greedy algorithm provides a (1-1/e-ε)-approximate solution, where ε is an error term caused by the intractable nature of influence spread computation. Previous work often utilizes an unbiased estimator that relies on the chosen edges for influence estimation, resulting in non-submodular estimate with respect to edge selection. To ensure the overall error being bounded by ε, such an estimator requires Θ(ε/k) multiplicative error for each estimation, incurring prohibitive overhead. Meanwhile, some other work approximates IMA via conventional influence maximization (IM) on an augmented graph by adding a new node for every edge candidate, leading to heavy extra sampling due to a significant increase in graph size. To address these challenges, we design a novel unbiased estimator on the original graph that is independent of the chosen edges by leveraging the tractability of one-hop influence computation. We show that the estimate via our estimator is submodular so that it enables the estimate of all k edges in a whole with a bounded estimation error of Θ(ε), saving O(k2) time compared to the chosen-edge-dependent estimator while retaining the same graph size. Moreover, we propose several techniques based on the properties of our estimator to further speed up the greedy selection. Putting it together, we develop a scalable algorithm for the IMA problem, namely ScaLIM. Finally, extensive experiments are conducted to validate the effectiveness and efficiency of our proposed approach, e.g., ScaLIM is faster than baselines by nearly two orders of magnitude.
Counterfactual Fairness Through Transforming Data Orthogonal to Bias
Authors: Shuyi Chen, Shixiang Zhu
Venue: KDD
Year: 2025

Machine learning models have demonstrated exceptional capabilities in solving complex problems across a variety of domains. However, these models can sometimes exhibit biased decision-making, leading to unequal treatment of different groups. Despite substantial research on counterfactual fairness, existing methods remain underdeveloped in addressing the impact of multivariate and continuous sensitive variables on decision-making outcomes. To tackle this gap, we propose a novel data pre-processing algorithm, Orthogonal to Bias (OB), which is designed to eliminate the influence of a group of continuous sensitive variables, thereby promoting counterfactual fairness in machine learning applications. Our approach, based on the assumption of an elliptical distribution within a structural causal model (SCM), shows that counterfactual fairness can be achieved by ensuring the data is orthogonal to the observed sensitive variables. The OB algorithm is model-agnostic, making it applicable to a wide range of machine learning models and tasks. To enhance numerical stability, we also introduce a sparse variant that incorporates regularization. Empirical evaluations on both simulated and real-world datasets-spanning scenarios with both discrete and continuous sensitive variables-demonstrate that our method effectively promotes fairer outcomes without compromising predictive accuracy.
AffinityTune: A Prompt-Tuning Framework for Few-Shot Anomaly Detection on Graphs
Authors: Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang
Venue: KDD
Year: 2025

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces a unified task framework grounded in affinity judgment, incorporating multi-granularity tasks (''node vs. adjacent subgraph'' and ''node vs. community'') to learn diverse anomaly priors. % For downstream tasks, we reformulate binary classification into a ''node vs. global representation'' affinity judgment, mapping anomalies to a hypersphere in feature space. % We also design learnable task heads to synergize multiple tasks and propose FlexPrompt, a flexible prompt-tuning strategy for fine-grained adaptation to downstream tasks. % Extensive experiments on real-world and synthetic datasets demonstrate that AffinityTune significantly outperforms existing methods in detection efficacy. % Our contributions include a unified task framework, multi-granularity tasks, and the FlexPrompt strategy, offering a parameter-efficient and adaptable solution for few-shot graph anomaly detection.
EMD-Period: Detecting Multi-periodicity in Industrial Cloud Clusters via Time-Frequency Decomposition
Authors: Qiming Chen, Yingying Zhang, Qingsong Wen, Liang Sun
Venue: KDD
Year: 2025

Accurate periodicity detection of performance metrics in cloud platform is essential for enhancing monitoring accuracy and ensure service quality of cloud computing. However, in real-world industrial settings, such as large-scale cloud computing platform MaxCompute at Alibaba Cloud, performance metrics often exhibit complex multi-periodicity and temporal nonstationarity. Additionally, these metrics are frequently contaminated by noise and anomalies, rendering traditional periodicity detection methods ineffective. To address these challenges, this paper proposes a robust and general period detection method with low deployment cost and high usability. The method employs Empirical Mode Decomposition (EMD) to decompose complex time series into Intrinsic Mode Functions (IMFs), isolating noise, periodic, and trend components. Significant periods are identified using statistical tests to eliminate irrelevant components, while an automatic clustering mechanism mitigates the mode-mixing problem inherent in EMD. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method outperforms state-of-the-art periodicity detection techniques, achieving over 10% and 17% performance improvements in public single and multiple periodicity detection tasks, respectively. Deployed in Alibaba Cloud's MaxCompute platform, it monitors millions of tasks, resulting in a 7.8% increase in fault detection coverage and enabling faults to be detected 13.15% more rapidly. The method's low deployment cost and user-friendly implementation make it highly accessible and practical for diverse cloud monitoring applications.
Seeing the Unseen in Micro-Video Popularity Prediction: Self-Correlation Retrieval for Missing Modality Generation
Authors: Zhangtao Cheng, Jian Lang, Ting Zhong, Fan Zhou
Venue: KDD
Year: 2025

Micro-video popularity prediction (MVPP) plays a crucial role in numerous real-world applications, including product marketing and recommendation systems. While existing methodologies predominantly assume complete modalities during multimodal learning, this assumption often fails to hold in practical scenarios due to various constraints, such as privacy concerns or data integrity issues. To address this limitation, we propose SCRAG, a novel Self-Correlation Retrieval-Augmented Generative framework designed to enhance missing-modality robustness in MVPP. SCRAG operates in a retrieval-guided generation manner that explores relevant knowledge to enhance the reconstruction of missing content, which consists of two primary components: (1) a self-correlation retriever and (2) a multimodal mixture-of-experts generator. It first acquires instances pertinent to the missing content through multimodal prompt alignment. Subsequently, the generator extracts contextual modal information from the retrieved context-rich instances. By learning the joint distribution of modalities, SCRAG effectively recovers missing content and addresses the modal heterogeneity challenge inherent in cross-modal generation approaches. Extensive experiments conducted on three real-world datasets demonstrate that SCRAG consistently outperforms state-of-the-art baselines, underscoring its effectiveness in handling incomplete modalities and improving the accuracy of micro-video popularity prediction.
Generative Thinking, Corrective Action: User-Friendly Composed Image Retrieval via Automatic Multi-Agent Collaboration
Authors: Zhangtao Cheng, Yuhao Ma, Jian Lang, Kunpeng Zhang, Ting Zhong, Yong Wang, Fan Zhou
Venue: KDD
Year: 2025

Zero-shot composed image retrieval (ZS-CIR) is a challenging task that aims to retrieve images similar to a composed query of a reference image and a description, without relying on training on triplet datasets. Existing methods for this task often rely on predefined, fixed retrieval processes that combine the image and the modified text through hand-crafted templates, which suffer from two main issues: non-adaptive retrieval queries and user-unfriendly retrieval processes. To address these limitations, we propose a novel framework - Automatic Multi-Agent Collaboration for Zero-Shot Composed Image Retrieval (AutoCIR). AutoCIR consists of three training-free agents - a planner, a retriever, and a corrector - that work together to iteratively identify and rectify mismatches. The planner guides the retriever by generating a customized target caption for the composed query and further refines this caption to resolve any semantic discrepancies based on feedback. The corrector, equipped with a chain-of-thought reasoning mechanism, conducts an in-depth evaluation of the retrieved results and generates appropriate self-correction actions. Extensive experiments on three benchmarks demonstrate that AutoCIR consistently outperforms previous competitive methods for ZS-CIR.