OpenRaiser/Intern-Atlas huggingface dataset:
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
Authors: Xinyi Gao, Yayong Li, Tong Chen, Guanhua Ye, Wentao Zhang, Hongzhi Yin
Venue: KDD
Year: 2025

With the increasing computation of training graph neural networks (GNNs) on large-scale graphs, graph condensation (GC) has emerged as a promising solution to synthesize compact, substitute graphs of the large-scale original graphs for efficient GNN training. However, these condensed graphs are specifically designed for the node classification task, significantly limiting the versatility of the synthesized data across various downstream tasks. This limitation predominantly stems from the reliance of existing GC methods on classification as the surrogate task for optimization, which leads to an excessive dependence on node labels and restricts their utility in label-scarcity scenarios. More critically, this surrogate task tends to overfit class-specific information within the condensed graph, consequently restricting the generalization capabilities of GC for other downstream tasks. To address these challenges, we introduce Contrastive Graph Condensation (CTGC), which adopts a self-supervised surrogate task to extract critical, causal information from the original graph and enhance the cross-task generalizability of the condensed graph. Specifically, CTGC employs a dual-branch framework to disentangle the generation of the node attributes and graph structures, where a dedicated structural branch is designed to explicitly encode geometric information through nodes' positional embeddings. By implementing an alternating optimization scheme with contrastive loss terms, CTGC promotes the mutual enhancement of both branches and facilitates high-quality graph generation through the model inversion technique. Extensive experiments demonstrate that CTGC excels in handling various downstream tasks with a limited number of labels, consistently outperforming state-of-the-art GC methods.
FedGuCci: Making Local Models More Connected in Landscape for Federated Learning
Authors: Zexi Li, Jie Lin, Zhiqi Li, Didi Zhu, Tao Shen, Tao Lin, Chao Wu, Nicholas D. Lane
Venue: KDD
Year: 2025

Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's global model has a large gap compared with centralized training, which is its bottleneck for broader applications. In this paper, we study and improve FL's generalization through a fundamental ''connectivity'' perspective, which means how the local models are connected in the parameter region and fused into a generalized global model. The term ''connectivity'' is derived from linear mode connectivity (LMC), studying the interpolated loss landscape of two different solutions (e.g., modes) of neural networks. Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL). Based on the findings, we propose FedGuCci(+), improving group connectivity for better generalization. It is shown that our methods can boost the generalization of FL under client heterogeneity across various tasks (4 CV datasets and 6 NLP datasets) and model architectures (e.g., ViTs and PLMs). The code is available here: https://github.com/ZexiLee/fedgucci.
Optimizing Case-Based Reasoning System for Functional Test Script Generation with Large Language Models
Authors: Siyuan Guo, Huiwu Liu, Xiaolong Chen, Yuming Xie, Liang Zhang, Tao Han, Hechang Chen, Yi Chang, Jun Wang
Venue: KDD
Year: 2025

In this work, we explore the potential of large language models (LLMs) for generating functional test scripts, which necessitates understanding the dynamically evolving code structure of the target software. To achieve this, we propose a case-based reasoning (CBR) system utilizing a 4R cycle (i.e., retrieve, reuse, revise, and retain), which maintains and leverages a case bank of test intent descriptions and corresponding test scripts to facilitate LLMs for test script generation. To improve user experience further, we introduce Re4, an optimization method for the CBR system, comprising reranking-based retrieval finetuning and reinforced reuse finetuning. Specifically, we first identify positive examples with high semantic and script similarity, providing reliable pseudo-labels for finetuning the retriever model without costly labeling. Then, we apply supervised finetuning, followed by a reinforcement learning finetuning stage, to align LLMs with our production scenarios, ensuring the faithful reuse of retrieved cases. Extensive experimental results on two product development units from Huawei Datacom demonstrate the superiority of the proposed CBR+Re4. Notably, we also show that the proposed Re4 method can help alleviate the repetitive generation issues with LLMs.
ConciseExplain: Reducing Redundancy and Spuriousness in Persuasive Recommendation Explanation
Authors: Yixuan Cao, Juyao Liu, Haodong Wang, Jian Wang, Kun Wan, Gang Xiao, Ping Luo
Venue: KDD
Year: 2025

Recommendation systems are effective tools for information filtering and discovery. These systems are widely applied across various consumer sectors and hold significant potential for applications in professional domains to enhance work efficiency. However, supporting decision-making in professional contexts requires not only providing recommendation results but also offering explanations to persuade users to adopt the suggestions. Taking the task in the primary bond market as an example, where sales staff seek potential investors for bonds, this paper presents the development and deployment of a recommendation system designed for a professional setting. The system provides a set of key features as explanations for its recommendations. In this process, we observe that current explanation methods may select redundant and spurious features, which can undermine the persuasive impact of the explanations. To address this issue, we propose a method named ConciseExplain, which leverages a mask training strategy and gradient descent to directly identify a concise set of features. We conduct experiments on real-world and synthetic datasets. Our method achieves relative improvements of 6.1% and 12.4% over the best-performing baseline on redundant and spurious metrics, respectively. Our method also outperforms the baseline method in online manual evaluations. Moreover, during the one-year official deployment of our system at China Securities Co., Ltd. (a leading brokerage firm in China), we observed a continuous improvement in the accuracy of the recommendation system. This suggests that, with concise explanations, a positive feedback loop might be established between recommendation outcomes and investment decisions.
CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies
Authors: Brian M. Cho, Ana-Roxana Pop, Kyra Gan, Sam Corbett-Davies, Israel Nir, Ariel Evnine, Nathan Kallus
Venue: KDD
Year: 2025

When modifying existing policies in high-risk settings, it is often necessary to ensure with high certainty that the newly proposed policy improves upon a baseline, such as the status quo. In this work, we consider the problem of safe policy improvement, where one only adopts a new policy if it is deemed to be better than the specified baseline with at least a pre-specified probability. We focus on threshold policies, a ubiquitous class of policies with applications in economics, healthcare, and digital advertising. Existing methods rely on potentially underpowered safety checks and limit the opportunities for finding safe improvements, so too often they must revert to the baseline to maintain safety. We overcome these issues by leveraging the most powerful safety test in the asymptotic regime and allowing for multiple candidates to be tested for improvement over the baseline. We show that in adversarial settings, our approach controls the rate of adopting a policy worse than the baseline to the pre-specified error level, even in moderate sample sizes. We present CSPI and CSPI-MT, two novel algorithms for selecting cutoff(s) to maximize the policy improvement from baseline. We demonstrate through both synthetic and external datasets that our approaches improve both the detection rates of safe policies and the realized improvement, particularly under stringent safety requirements and low signal-to-noise conditions.
Toast: Task-Oriented Multi-dimensional Augmentation for Spatio-Temporal Trajectory Data
Authors: Junhao Zhu, Tao Wang, Lu Chen, Ziquan Fang, Yunjun Gao, Tianyi Li
Venue: KDD
Year: 2025

With the growing availability of large data repositories within and across organizations, it is becoming feasible to selectively acquire data in the wild for data augmentation, tailored to specific downstream tasks. However, current methodologies concentrate primarily on single-dimensional augmentation tasks for tabular data, such as increasing the number of data points or enriching features. These approaches are not designed for trajectory data, which cannot fully utilize its spatio-temporal characteristics, resulting in suboptimal performance. To address this gap, we present Toast, an iterative framework of task-oriented multi-dimensional augmentation for spatio-temporal trajectory data. Toast supports all three augmentation operations and consists of three core components: (1) augmentation candidate search, which identifies unionable or joinable augmentation candidates in the wild; (2) augmentation candidate selection, which defines and selects the ''best'' augmentations that offer the greatest benefit to downstream tasks; (3) augmentation type selection, which determines the most cost-effective type of augmentation to perform based on a cost model. Extensive experiments on four classical trajectory data mining applications and datasets demonstrate that Toast can enhance model performance by 5%-15% and achieve more than a 2× improvement in efficiency.
The 2nd Workshop on Large Language Models for E-Commerce
Authors: Haoyu Han, Fali Wang, Chen Luo, Hui Liu, Jing Huang, Zhen Li, Zhenwei Dai, Qi He, Yiwei Sun, Dawei Yin, Suhang Wang, Jiliang Tang, Jian Pei, Xianfeng Tang
Venue: KDD
Year: 2025

Large Language Models (LLMs) are revolutionizing E-Commerce by enabling product recommendation, search, classification, question answering, and advertising applications. Their increasing adoption in real-world systems underscores their potential; however, challenges persist in ensuring accuracy, efficiency, fairness, and privacy. This workshop aims to bring together researchers and industry practitioners to explore both the limitations and opportunities of LLMs in e-commerce. The workshop seeks to foster collaboration, bridge the gap between academia and industry, and drive innovation in the application of LLMs to E-Commerce through discussions on model design, algorithmic advancements, and practical deployment.
Advancing Graph Foundation Models: A Data-Centric Perspective
Authors: Yuhan Li, Yuyao Wang, Jianheng Tang, Heng Chang, Yuxiang Ren, Jia Li
Venue: KDD
Year: 2025

Recently, Graph Foundation Models (GFMs) have emerged as a significant research topic in graph machine learning. Compared with traditional graph neural networks, GFMs demonstrate impressive zero-shot generalization across different domains and tasks through large-scale pre-training on extensive and diverse graph data. Despite the initial success of pre-training, existing GFMs face challenges such as extreme time consumption and the presence of redundancy and noise in pre-training data. To alleviate these issues, we present the first exploration of data-centric GFM, which aims to optimize pre-training data (i.e., a set of subgraphs) to establish a more efficient GFM while maintaining robust performance across various downstream tasks. We propose DCGFM, a plug-and-play approach for Data-Centric GFM that incorporates the idea of data pruning to remove redundant and less informative subgraphs from the pre-training data, thereby improving both efficiency and effectiveness. Specifically, DCGFM consists of two components: (1) a model-agnostic hard pruning module that filters out subgraphs with lower informativity scores by considering both the semantics and structures of subgraphs; and (2) a model-aware soft pruning module that dynamically prunes subgraphs with lower loss values in each pre-training epoch with a gradient rescaling strategy. Extensive experiments on representative GFM backbones demonstrate DCGFM's efficiency and effectiveness. Remarkably, DCGFM achieves even better performance using only 30% of the pre-training data. Codes and data are available at https://github.com/Yuhan1i/DCGFM.
FedKDD 2025: The 2025 International Joint Workshop on Federated Learning for Data Mining and Graph Analytics
Authors: Carl Yang, Guancheng Wan, Zhuangdi Zhu, Zheng Xu, Junyuan Hong, Nathalie Baracaldo, Neil Shah, Salman Avestimehr, Jiayu Zhou
Venue: KDD
Year: 2025

Deep Learning has facilitated various high-stakes applications such as crime detection, urban planning, drug discovery, and healthcare. Its continuous success hinges on learning from massive data in miscellaneous sources, ranging from data with independent distributions to graph-structured data capturing intricate inter-sample relationships. Scaling up the data access requires global collaboration from distributed data owners. Yet, centralizing all data sources to an untrustworthy centralized server will put users' data at risk of privacy leakage or regulation violation. Federated Learning (FL) is a de facto decentralized learning framework that enables knowledge aggregation from distributed users without exposing private data. Though promising advances are witnessed for FL, new challenges are emerging when integrating FL with the rising needs and opportunities in data mining, graph analytics, foundation models, generative AI, and new interdisciplinary applications in science. By hosting this workshop, we aim to attract a broad range of audiences, including researchers and practitioners from academia and industry interested in the emergent challenges in FL. As an effort to advance the fundamental development of FL, this workshop will encourage ideas exchange on the trustworthiness, scalability, and robustness of distributed data mining and graph analytics and their emergent challenges.
GDendrite: On Heterophilous Graph Contexts Mining with Versatile Neural Dendrites Framework
Authors: Ruikun Li, Ye Xiao, Xiaoxiao Ma, Andrey Vasnev, Junbin Gao
Venue: KDD
Year: 2025

A significant challenge for Graph Neural Networks (GNNs) lies in accurately performing node classification tasks on heterophilous graphs. Traditional GNN models, which largely rely on the assumption of homophily, often struggle under heterophilous conditions due to the inherent low-pass filtering nature during message-passing among neighboring nodes. Motivated by these limitations, our research explores the dendritic structures found in real neurons, which dynamically amplify and inhibit signals. We demonstrate how the biophysical properties of dendrites enable GNNs to adaptively modulate the Dirichlet energy of graph representations based on the specific context, providing a theoretical justification for integrating neural dendrites into GNNs to improve learning on heterophilous graphs. In this work, we introduce the GDendrite framework, which dynamically adjusts GNN's output response to the various contexts. Our empirical studies, conducted across 13 homophilous and heterophilous benchmark datasets, show that our framework significantly enhances the performance of traditional GNNs in heterophilous settings and outperforms state-of-the-art models in most cases.
TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations
Authors: Yihang Lu, Yangyang Xu, Qitao Qin, Xianwei Meng
Venue: KDD
Year: 2025

Recent deep learning models for Long-term Time Series Forecasting (LTSF) often emphasize complex, handcrafted designs, while simpler architectures like linear models or MLPs have often outperformed these intricate solutions. In this paper, we revisit and organize the core ideas behind several key techniques, such as redundancy reduction and multi-scale modeling, which are frequently employed in advanced LTSF models. Our goal is to streamline these ideas for more efficient deep learning utilization. To this end, we introduce TimeCapsule, a model built around the principle of high-dimensional information compression that unifies these techniques in a generalized yet simplified framework. Specifically, we model time series as a 3D tensor, incorporating temporal, variate, and level dimensions, and leverage mode production to capture multi-mode dependencies while achieving dimensionality compression. We propose an internal forecast within the compressed representation domain, supported by the Joint-Embedding Predictive Architecture (JEPA), to monitor the learning of predictive representations. Extensive experiments on challenging benchmarks demonstrate the versatility of our method, showing that TimeCapsule can achieve state-of-the-art performance. The code is available at: https://github.com/Luoauoa/TimeCapsule.git.
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
Authors: Maolin Wang, Yutian Xiao, Binhao Wang, Sheng Zhang, Shanshan Ye, Wanyu Wang, Hongzhi Yin, Ruocheng Guo, Zenglin Xu
Venue: KDD
Year: 2025

Modern recommendation systems face significant challenges in processing multimodal sequential data, particularly in temporal dynamics modeling and information flow coordination. Traditional approaches struggle with distribution discrepancies between heterogeneous features and noise interference in multimodal signals. We propose FindRec (Flexible unified information disentanglement for multi-modal sequential Rec ommendation), introducing a novel ''information flow-control-output'' paradigm. The framework features two key innovations: (1) A Stein kernel-based Integrated Information Coordination Module (IICM) that theoretically guarantees distribution consistency between multimodal features and ID streams, and (2) A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance. Our approach leverages multi-head subspace decomposition for routing stability and RBF-Stein gradient for unbiased distribution alignment, enhanced by linear-complexity Mamba layers for efficient temporal modeling. Extensive experiments on three real-world datasets demonstrate FindRec's superior performance over state-of-the-art baselines, particularly in handling long sequences and noisy multimodal inputs. Our framework achieves both improved recommendation accuracy and enhanced model interpretability through its modular design. The implementation code is available anonymously online for easy reproducibility https://github.com/Applied-Machine-Learning-Lab/FindRec.
Efficient Sphere-Effect Based Information Diffusion Prediction on Large-scale Social Networks
Authors: Zihan Feng, Yajun Yang, Xin Huang, Hong Gao, Liping Jing, Qinghua Hu
Venue: KDD
Year: 2025

Information diffusion prediction is fundamental for forecasting user participation in information sharing on social networks, such as retweets on Twitter. Existing methods typically extract user relationships from social networks and historical interactions, while further capturing contextual information within the specific diffusion process. However, these methods have several limitations: (1) They often utilize sequential diffusion process for prediction and simplify differentiated influences among participants; (2) They capture user relationships on the entire graph for all users, in which most information is not necessary for a specific diffusion process and is too inefficient for real-world large-scale networks. To tackle these limitations, we propose a novel and scalable model SILN, for sphere-based information diffusion prediction on large social networks. Specifically, SILN features three components. First, we integrate two kinds of sphere effects in terms of structural and temporal views, which learn an enhanced cascade representation. Second, SILN designs an efficient learning scheme based on the cascade-specific subgraph, which significantly reduces the entire graph computation to smaller subgraphs. Third, to facilitate subgraph extraction, we develop an optimized graph storage technique to allow constant-time neighbor access and reduce the storage cost by about 30% in practice. Extensive experiments on six real-world datasets validate that SILN consistently outperforms seven state-of-the-art competitors in prediction performance while exhibiting exceptional time and space efficiency on million-node social networks.
Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head
Authors: Penghui Yang, Chen-Chen Zong, Sheng-Jun Huang, Lei Feng, Bo An
Venue: KDD
Year: 2025

Traditional knowledge distillation focuses on aligning the student's predicted probabilities with both ground-truth labels and the teacher's predicted probabilities. However, the transition to predicted probabilities from logits would obscure certain indispensable information. To address this issue, it is intuitive to additionally introduce a logit-level loss function as a supplement to the widely used probability-level loss function, for exploiting the latent information of logits. Unfortunately, we empirically find that the amalgamation of the newly introduced logit-level loss and the previous probability-level loss will lead to performance degeneration, even trailing behind the performance of employing either loss in isolation. We attribute this phenomenon to the collapse of the classification head, which is verified by our theoretical analysis based on the neural collapse theory. Specifically, the gradients of the two loss functions exhibit contradictions in the linear classifier yet display no such conflict within the backbone. Drawing from the theoretical analysis, we propose a novel method called dual-head knowledge distillation, which partitions the linear classifier into two classification heads responsible for different losses, thereby preserving the beneficial effects of both losses on the backbone while eliminating adverse influences on the classification head. Extensive experiments validate that our method can effectively exploit the information inside the logits and achieve superior performance against state-of-the-art counterparts
An Auto-Coupon Framework for Advertiser Retention in Online Advertising
Authors: Yidan Xing, Haoqi Zhang, Yangsu Liu, Dagui Chen, Zhenzhe Zheng, Fan Wu
Venue: KDD
Year: 2025

The online advertising platform serves as the intermediary between advertisers and users to facilitate the product trading process and generate profits. While the vitality of both the user and advertiser community is crucial to the long-term development of platforms, compared with the enduring efforts to improve user experience, little attention has been given to methods for attracting or retaining advertisers. To address this oversight, in this work, we investigate the first intelligent solution to the advertiser retention problem in online advertising. Motivated by advertiser surveys and data analytics, we first define a set of short-term ad performance metrics as the key retention metrics we need to optimize for advertisers. In consideration of the desirable properties of a retention framework, we propose an Auto-Coupon module to distribute impression-level virtual coupons to selected advertisers during the bidding process. We decompose the resulted retention optimization problem into two stages, and adopt feedback control as well as reinforcement learning methods to realize personalized coupon distribution. The offline experiments demonstrate the effectiveness of our methods in boosting the ad performance of churning advertisers, and the large-scale online A/B test further justifies the improvement in advertisers' willingness to stay in the platform.