OpenRaiser/Intern-Atlas huggingface dataset:
PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep IP Protection
Authors: Enyan Dai, Minhua Lin, Suhang Wang
Venue: KDD
Year: 2025

Pretraining on Graph Neural Networks (GNNs) has shown great power in facilitating various downstream tasks. As pretraining generally requires huge amount of data and computational resources, the pretrained GNNs are high-value Intellectual Properties (IP) of the legitimate owner. However, adversaries may illegally copy and deploy the pretrained GNN models for their downstream tasks. Though initial efforts have been made to watermark GNN classifiers for IP protection, these methods are not applicable to self-supervised pretraining of GNN models. Hence, in this work, we propose a novel framework named PreGIP to watermark the pretraining of GNN encoder for IP protection while maintaining the high-quality of the embedding space. PreGIP incorporates a task-free watermarking loss to watermark the embedding space of pretrained GNN encoder. A finetuning-resistant watermark injection is further deployed. Theoretical analysis and extensive experiments show the effectiveness of PreGIP. The code can be find in https://anonymous.4open.science/r/PreGIP-semi/ and https://anonymous.4open.science/r/PreGIP-transfer/.
A Unified Online-Offline Framework for Co-Branding Campaign Recommendations
Authors: Xiangxiang Dai, Xiaowei Sun, Jinhang Zuo, Xutong Liu, John C. S. Lui
Venue: KDD
Year: 2025

Co-branding has become a vital strategy for businesses aiming to expand market reach within recommendation systems. However, identifying effective cross-industry partnerships remains challenging due to resource imbalances, uncertain brand willingness, and ever-changing market conditions. In this paper, we provide the first systematic study of this problem and propose a unified online-offline framework to enable co-branding recommendations. Our approach begins by constructing a bipartite graph linking ''initiating'' and ''target'' brands to quantify co-branding probabilities and assess market benefits. During the online learning phase, we dynamically update the graph in response to market feedback, while striking a balance between exploring new collaborations for long-term gains and exploiting established partnerships for immediate benefits. To address the high initial co-branding costs, our framework mitigates redundant exploration, thereby enhancing short-term performance while ensuring sustainable strategic growth. In the offline optimization phase, our framework consolidates the interests of multiple sub-brands under the same parent brand to maximize overall returns, avoid excessive investment in single sub-brands, and reduce unnecessary costs associated with over-prioritizing a single sub-brand. We present a theoretical analysis of our approach, establishing a highly nontrivial sublinear regret bound for online learning in the complex co-branding problem, and enhancing the approximation guarantee for the NP-hard offline budget allocation optimization. Experiments on both synthetic and real-world co-branding datasets demonstrate the practical effectiveness of our framework, with at least 12% improvement.
Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks
Authors: Mohsen Dehghankar, Abolfazl Asudeh
Venue: KDD
Year: 2025

Large language models (LLMs) have quickly emerged as practical and versatile tools that provide new solutions for a wide range of domains. In this paper, we consider the application of LLMs on symmetric tasks where a query is asked on an (unordered) bag of elements. Examples of such tasks include answering aggregate queries on a dataset. In general, when the bag contains a large number of elements, LLMs tend to overlook some elements, leading to challenges in generating accurate responses to the query. LLMs receive their inputs as ordered sequences. On the other hand, the input of a symmetric task is not ordered; hence reordering should not affect the LLM's response. Observing that LLMs are less likely to miss elements at certain positions of the input, we introduce the problem of LLM input reranking: to find a ranking of the input that maximizes the LLM's accuracy for the given query without making explicit assumptions about the query. Finding the optimal ranking requires identifying (i) the relevance of each input element for answering the query and (ii) the importance of each rank position for the LLM's attention. We develop algorithms for estimating these values efficiently utilizing a helper LLM. We conduct comprehensive experiments on different synthetic and real datasets to validate our proposal and to evaluate the effectiveness of our proposed algorithms. Our experiments confirm that our reranking approach improves the accuracy of the LLMs on symmetric tasks by up to 99% proximity to the optimum upper bound.
Fair Set Cover
Authors: Mohsen Dehghankar, Rahul Raychaudhury, Stavros Sintos, Abolfazl Asudeh
Venue: KDD
Year: 2025

The potential harms of algorithmic decisions have ignited algorithmic fairness as a central topic in computer science. One of the fundamental problems in computer science is Set Cover, which has numerous applications with societal impacts, such as assembling a small team of individuals that collectively satisfy a range of expertise requirements. However, despite its broad application spectrum and significant potential impact, set cover has yet to be studied through the lens of fairness. Therefore, in this paper, we introduce Fair Set Cover, which aims not only to cover with a minimum-size set but also to satisfy demographic parity in its selection of sets. To this end, we develop multiple versions of fair set cover, study their hardness, and devise efficient approximation algorithms for each variant. Notably, under certain assumptions, our algorithms always guarantee zero-unfairness, with only a small increase in the approximation ratio compared to regular set cover. Furthermore, our experiments on various data sets and across different settings confirm the negligible price of fairness, as (a) the output size increases only slightly (if any) and (b) the time to compute the output does not significantly increase.
TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation
Authors: Bowen Deng, Chang Xu, Hao Li, Yu-Hao Huang, Min Hou, Jiang Bian
Venue: KDD
Year: 2025

Synthetic Electronic Health Record (EHR) time-series generation is crucial for advancing clinical machine learning models, as it helps address data scarcity by providing more training data. However, most existing approaches focus primarily on replicating statistical distributions and temporal dependencies of real-world data. We argue that fidelity to observed data alone does not guarantee better model performance, as common patterns may dominate, limiting the representation of rare but important conditions. This highlights the need for generate synthetic samples to improve performance of specific clinical models to fulfill their target outcomes. To address this, we propose TarDiff, a novel target-oriented diffusion framework that integrates task-specific influence guidance into the synthetic data generation process. Unlike conventional approaches that mimic training data distributions, TarDiff optimizes synthetic samples by quantifying their expected contribution to improving downstream model performance through influence functions. Specifically, we measure the reduction in task-specific loss induced by synthetic samples and embed this influence gradient into the reverse diffusion process, thereby steering the generation towards utility-optimized data. Evaluated on six publicly available EHR datasets, TarDiff achieves state-of-the-art performance, outperforming existing methods by up to 20.4% in AUPRC and 18.4% in AUROC. Our results demonstrate that TarDiff not only preserves temporal fidelity but also enhances downstream model performance, offering a robust solution to data scarcity and class imbalance in healthcare analytics.
CompilerDream: Learning a Compiler World Model for General Code Optimization
Authors: Chaoyi Deng, Jialong Wu, Ningya Feng, Jianmin Wang, Mingsheng Long
Venue: KDD
Year: 2025

Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the optimization passes applied to the code. While most compilers rely on fixed pass sequences, current methods to find the optimal sequence for specific programs either employ impractically slow search algorithms or learning methods that struggle to generalize to code unseen during training. To address these challenges, we introduce CompilerDream, the first world-model-based approach for general code optimization. CompilerDream features a compiler world model with a reward smoothing technique, enabling accurate simulation of optimization processes. Built on this model, code optimization agents can then be constructed via value prediction or direct optimization sequence generation. Trained on a large-scale program dataset, these agents serve as versatile code optimizers across diverse application scenarios and source-code languages. Our extensive experiments highlight CompilerDream's strong optimization capabilities for autotuning, where it leads the CompilerGym leaderboard. More importantly, the zero-shot generalization ability of large-scale trained compiler world model and agent, excels across diverse datasets, surpassing LLVM's built-in optimizations and state-of-the-art methods in both settings of value prediction and end-to-end code optimization.
Marionette: Fine-Grained Conditional Generative Modeling of Spatiotemporal Human Trajectory Data Beyond Imitation
Authors: Bangchao Deng, Ling Ding, Lianhua Ji, Chunhua Chen, Xin Jing, Bingqing Qu, Dingqi Yang
Venue: KDD
Year: 2025

Synthetic human trajectory data becoming increasingly prominent in various applications, including urban planning, traffic control, and crowd monitoring. Recent neural generative models for human trajectory data mostly follow an unconditional generative paradigm that relies on a pure data-driven imitative learning scheme, without considering the rich context of human mobility (e.g., social events or weather conditions) which may significantly impact the underlying human mobility patterns. Against this background, we propose Marionette, a Manipulatable generative model for human trajectory data with fine-grained conditions. Specifically, Marionette integrates both global and partial mobility-related contexts and extracts both sequence-level and event-level conditions. Afterward, it designs fine-grained and cascading conditioning mechanisms for modeling the temporal and spatial dynamics based on diffusion-alike Temporal Point Processes (TPPs) and discrete diffusion models, respectively, offering fine-grained controllable generative modeling of human trajectory data with both global and partial mobility-related contexts. We conduct a thorough evaluation on two real-world human trajectory datasets against a sizeable collection of baselines. Results show that our Marionette consistently outperforms the best baselines by 13.96-54.13% on statistical and distributional similarity metrics and by 9.36-40.63% in task-based data utility evaluation. Ablation studies verify our key design choices. Case studies also demonstrate the manipulability of Marionette in generating data in previously unseen scenarios.
Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus
Authors: Bangchao Deng, Xin Jing, Tianyue Yang, Bingqing Qu, Dingqi Yang, Philippe Cudré-Mauroux
Venue: KDD
Year: 2025

Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human trajectory data is generated to simulate as close as possible to real-world human trajectories, often under summary statistics and distributional similarities. However, these similarities oversimplify complex human mobility patterns (a.k.a. ''Datasaurus''), resulting in intrinsic biases in both generative model design and benchmarks of the generated trajectories. Against this background, we propose MIRAGE, a huMan-Imitative tRAjectory GenErative model designed as a neural Temporal Point Process integrating an Exploration and Preferential Return model. It imitates the human decision-making process in trajectory generation, rather than fitting any specific statistical distributions as traditional methods do, thus avoiding the Datasaurus issue. We also propose a comprehensive task-based evaluation protocol beyond Datasaurus to systematically benchmark trajectory generative models on four typical downstream tasks, integrating multiple techniques and evaluation metrics for each task, to assess the ultimate utility of the generated trajectories. We conduct a thorough evaluation of MIRAGE on three real-world user trajectory datasets against a sizeable collection of baselines. Results show that compared to the best baselines, MIRAGE-generated trajectory data not only achieves the best statistical and distributional similarities with 59.0-67.7% improvement, but also yields the best performance in the task-based evaluation with 10.9-33.4% improvement. A series of ablation studies also validate the key design choices of MIRAGE.
Boost the Performance of Tabular Data Models with GPU Accelerated Feature Engineering
Authors: Chris Deotte, Ronay Ak
Venue: KDD
Year: 2025

Feature engineering remains a crucial technique for improving the performance of models trained on tabular data. Unlike computer vision and natural language processing, where deep learning models automatically extract hierarchical features from raw data, the most accurate tabular models, such as gradient boosted decision trees, still benefit significantly from manually crafted features. This is demonstrated in Team NVIDIA's many first-place data science competition victories. Fast experimentation in feature engineering is essential to quickly discover the most valuable features that improve model performance. In this tutorial, we use NVIDIA cuDF and cuML libraries to accelerate the experimentation pipeline on GPU, allowing us to search for and engineer new features more rapidly and build more accurate models faster. First, participants will be introduced to specific feature engineering techniques: normalization, binning, count encoding, and target encoding. These techniques will be taught using the Amazon product review dataset that contains product reviews from May 1996 through July 2014. Specifically, we will use the electronic category of this dataset that contains 1,689,188 reviews. Next, they will train gradient boosted decision tree models and support vector classification models with and without feature engineering. Participants will learn how engineered features can significantly boost the accuracy of ML models, and by the end of the tutorial, they will gain practical techniques that can be immediately applied to their real-world use cases.
Error Bounds for the Network Scale-Up Method
Authors: Sergio Díaz-Aranda, Juan Marcos Ramirez, Mohit Daga, Jaya Prakash Champati, Jose Aguilar, Rosa E. Lillo, Antonio Fernández Anta
Venue: KDD
Year: 2025

Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. This method involves querying a subset of network nodes about the number of their neighbors belonging to the hidden sub-population. In general, NSUM assumes that the social network topology and the hidden sub-population distribution are well-behaved; hence, the NSUM estimate is close to the actual value. However, bounds on NSUM estimation errors have not been analytically proven. This paper provides analytical bounds on the error incurred by the two most popular NSUM estimators. These bounds assume that the queried nodes accurately provide their degree and the number of neighbors belonging to the hidden sub-population. Our key findings are twofold. First, we show that when an adversary designs the network and places the hidden sub-population, then the estimate can be a factor of Ω(√n) off from the real value (in a network with n nodes). Second, we also prove error bounds when the underlying network is randomly generated, showing that a small constant factor can be achieved with high probability using samples of logarithmic size O(log n). We present improved analytical bounds for Erdős-Rényi and Scale-Free networks. Our theoretical analysis is supported by an extensive set of numerical experiments designed to determine the effect of the sample size on the accuracy of the estimates in both synthetic and real networks.
Differentially Private Synthetic Data Release for Topics API Outputs
Authors: Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andrés Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
Venue: KDD
Year: 2025

The analysis of the privacy properties of Privacy-Preserving Ads APIs is an area of research that has received strong interest from academics, industry, and regulators. Despite this interest, the empirical study of these methods is severely hindered by the lack of publicly available data. Reliable empirical analysis of the privacy properties of an API, in fact, requires access to a dataset consisting of realistic API outputs for a large collection of users; however, privacy concerns prevent the general release of such data to the public. In this work, we address this problem by developing a novel methodology to construct synthetic API outputs that are simultaneously realistic enough to enable accurate study and provide strong privacy protections. We focus on one of the Privacy-Preserving Ads APIs: the Topics API, part of Google Chrome's Privacy Sandbox, which enables interest-based advertising without relying on third-party cookies. We developed a methodology to generate a differentially-private dataset that closely matches the re-identification risk properties of the real Topics API data. The use of differential privacy provides strong theoretical bounds on the leakage of private user information from this release. Our methodology is based on first computing a large number of differentially-private statistics describing how output API traces evolve over time. Then, we design a parameterized distribution over sequences of API traces and optimize its parameters so that they closely match the statistics obtained. Finally, we create the synthetic data by drawing from this distribution. Our work is complemented by an open-source release of the anonymized dataset obtained by this methodology. We hope this will enable external researchers to analyze the API in-depth and replicate prior and future work on a realistic large-scale dataset. We believe that this work will contribute to fostering transparency regarding the privacy properties of Privacy-Preserving Ads APIs.
WikiRAG: Revisiting Wikidata KGC Datasets with Community Updates and Retrieval-Augmented Generation
Authors: Djellel Eddine Difallah
Venue: KDD
Year: 2025

Link prediction is an important task for knowledge graph completion and curation, and it has received significant attention from the research community. However, researchers often train and evaluate new models on small or outdated datasets that do not reflect the current state of knowledge, thereby disregarding new information and the rich textual content often linked to knowledge graphs. As a result, many opportunities to leverage these dimensions are missed. We introduce WikiRAG, a framework for knowledge completion and evaluation derived from Wikidata and Wikipedia, which enables research integrating retrieval techniques and large language models. Our framework combines the following contributions: (i) We revisit the Wikidata5M dataset by updating it to reflect the current state of Wikidata and providing automated tools for its periodic maintenance. (ii) We enrich the dataset with long-form textual content sourced from Wikipedia, enabling research that goes beyond traditional graph structures and shallow text methods toward dense retrieval techniques. (iii) We propose a simple yet effective baseline that leverages retrieval-augmented generation, demonstrating the utility of the dataset and integrating language model capabilities for link prediction. The revised dataset, coined Wikidata5M-RE, shows that the original graph grew by roughly 50% in the number of edges, while 10% of the edges have been removed. A comparative analysis of classic methods demonstrates that these changes can impact downstream task evaluation. Finally, our evaluation of WikiRAG's KGC method shows an improvement of up to 9% in link prediction accuracy over state-of-the-art baselines, setting the stage for a new avenue in knowledge completion that uses deep information extraction. The source code, data, and other artifacts have been made available on the project website: https://github.com/colab-nyuad/WikiRAG
Enhancing Microservices Anomaly Detection via Multimodal Data Fusion in the Wavelet Domain and Spatiotemporal Graph-based Diffusion Probabilistic Model
Authors: Kaiqi Ding, Yuanmu Ma, Zijian Song, Kaigui Bian
Venue: KDD
Year: 2025

Microservices architecture has become increasingly popular in modern software systems, yet its complexity also poses challenges for failure diagnosis. Existing literature has deficiencies in multimodal data mining, mainly in two aspects: first, the mining of multimodal data is insufficient, failing to fully exploit the rich information contained in different types of data; second, the analysis of spatiotemporal features of multimodal data is not thorough, failing to fully explore the potential associations of data in the temporal and spatial dimensions. To address these issues, this paper proposes a new method for anomaly detection in microservices. We transform three types of modal data into the wavelet domain and achieve fusion among modalities based on an attention mechanism, fully utilizing the inherent characteristics of multimodal data in the frequency and temporal domains. This fusion process can extract new information that does not exist in the original modalities, thereby enhancing the model's ability to detect anomalies. In addition, we propose a new diffusion probabilistic model (DDPM) based on spatiotemporal graphs, which combines spatiotemporal learning capabilities with the uncertainty measurement of DDPM to generate future samples in a non-autoregressive manner, achieving multi-horizon prediction. Experimental results show that our method significantly improves anomaly detection performance on three public datasets and a real-world production system, demonstrating its effectiveness in microservices anomaly detection.
KDD 2025 Workshop on Prompt Optimization
Authors: Haibo Ding, Shuai Wang, Kiran Ramnath, Yi Zhang, Lin Lee Cheong
Venue: KDD
Year: 2025

Prompt engineering plays a critical role in enabling the effective use of large language models (LLMs). LLMs exhibit unpredictable sensitivity to various input factors that may result in a performance gap between prompts that are semantically indistinguishable. Therefore, LLM researchers and practitioners often optimize their prompts in an ad hoc manner due to the lack of systematic methods. Prompt optimization remains an open problem due to the rapidly evolving landscape of NLP tasks, target LLMs, and associated best practices. To address this gap, we propose the first KDD workshop on Prompt Optimization. This workshop aims to bring together researchers and practitioners working on prompt design, automatic optimization, and evaluation, fostering the exchange of ideas and methodologies. By exploring topics such as discrete and soft prompt tuning, low-resource applications, we aim to establish best practices, identify challenges, and drive future research in this critical area.