OpenRaiser/Intern-Atlas huggingface dataset:
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
Authors: Zhan Cheng, Bolin Shen, Tianming Sha, Yuan Gao, Shibo Li, Yushun Dong
Venue: KDD
Year: 2025

Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs), where adversaries reconstruct surrogate models by querying the victim model. Existing defense mechanisms, such as watermarking and fingerprinting, suffer from poor real-time performance, susceptibility to evasion, or reliance on post-attack verification, making them inadequate for handling the dynamic characteristics of graph-based MEA variants. To address these limitations, we propose ATOM, a novel real-time MEA detection framework tailored for GNNs. ATOM integrates sequential modeling and reinforcement learning to dynamically detect evolving attack patterns, while leveraging k-core embedding to capture the structural properties, enhancing detection precision. Furthermore, we provide theoretical analysis to characterize query behaviors and optimize detection strategies. Extensive experiments on multiple real-world datasets demonstrate that ATOM outperforms existing approaches in detection performance, maintaining stable across different time steps, thereby offering a more effective defense mechanism for GMLaaS environments. Our source code is available at https://github.com/LabRAI/ATOM.
BTS: A Comprehensive Benchmark for Tie Strength Prediction
Authors: Xueqi Cheng, Catherine Yang, Yuying Zhao, Yu Wang, Hamid Karimi, Tyler Derr
Venue: KDD
Year: 2025

The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive Benchmark for Tie Strength prediction, aiming to establish a standardized foundation for evaluating and advancing TS prediction methodologies. Specifically, our contributions are: TS Pseudo-Label Techniques -- we categorize TS into seven standardized pseudo-labeling techniques based on prior literature; TS Dataset Collection -- we present a representative collection of three social networks and perform data analysis by investigating the class distributions and correlations across the generated pseudo-labels; TS Pseudo-Label Evaluation Framework -- we propose a standardized framework to evaluate the pseudo-label quality from the perspective of tie resilience; Benchmarking -- we evaluate existing tie strength prediction model performance using the BTS dataset collection, exploring the effects of different experiment settings, models, and evaluation criteria on the results. Furthermore, we derive key insights to enhance existing methods and shed light on promising directions for future research in this domain. The BTS dataset collection, along with the curation codes and experimental scripts, is all available at: https://github.com/XueqiC/Awesome-Tie-Strength-Prediction.
Auto-Bidding in Real-Time Auctions via Oracle Imitation Learning
Authors: Alberto Silvio Chiappa, Briti Gangopadhyay, Zhao Wang, Shingo Takamatsu
Venue: KDD
Year: 2025

Online advertising has become one of the most successful business models of the internet era. Impression opportunities are typically allocated through real-time auctions, where advertisers bid to secure advertisement slots. Deciding the best bid for an impression opportunity is challenging, due to the stochastic nature of user behavior and the variability of advertisement traffic over time. In this work, we propose a framework for training auto-bidding agents in multi-slot second-price auctions to maximize acquisitions (e.g., clicks, conversions) while adhering to budget and cost-per-acquisition (CPA) constraints. We exploit the insight that, after an advertisement campaign concludes, determining the optimal bids for each impression opportunity can be framed as a multiple-choice knapsack problem (MCKP) with a nonlinear objective. We propose an ''oracle'' algorithm that identifies a near-optimal combination of impression opportunities and advertisement slots, considering both past and future advertisement traffic data. This oracle solution serves as a training target for a student network which bids having access only to real-time information, a method we term Oracle Imitation Learning (OIL). Through numerical experiments, we demonstrate that OIL achieves superior performance compared to both online and offline reinforcement learning algorithms, offering improved sample efficiency. Notably, OIL shifts the complexity of training auto-bidding agents from crafting sophisticated learning algorithms to solving a nonlinear constrained optimization problem efficiently. Code available at https://github.com/sony/oil
Modeling Time-evolving Causality over Data Streams
Authors: Naoki Chihara, Yasuko Matsubara, Ren Fujiwara, Yasushi Sakurai
Venue: KDD
Year: 2025

Given an extensive, semi-infinite collection of multivariate coevolving data sequences (e.g., sensor/web activity streams) whose observations influence each other, how can we discover the time-changing cause-and-effect relationships in co-evolving data streams? How efficiently can we reveal dynamical patterns that allow us to forecast future values? In this paper, we present a novel streaming method, ModePlait, which is designed for modeling such causal relationships (i.e., time-evolving causality) in multivariate co-evolving data streams and forecasting their future values. The solution relies on characteristics of the causal relationships that evolve over time in accordance with the dynamic changes of exogenous variables. ModePlait has the following properties: (a) Effective: it discovers the time-evolving causality in multivariate co-evolving data streams by detecting the transitions of distinct dynamical patterns adaptively. (b) Accurate: it enables both the discovery of time-evolving causality and the forecasting of future values in a streaming fashion. (c) Scalable: our algorithm does not depend on data stream length and thus is applicable to very large sequences. Extensive experiments on both synthetic and real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods in terms of discovering the time-evolving causality as well as forecasting.
SkySearch: Satellite Video Search at Scale
Authors: Minyoung Choe, Geon Lee, Changhun Han, Suji Kim, Woong Hu, Hyebeen Hwang, Geunseok Park, Byeongyeon Kim, Hyesook Lee, Ha-Myung Park, Kijung Shin
Venue: KDD
Year: 2025

Satellite images play a crucial role in weather analytics, and recent advancements in satellite technology have significantly enhanced the accuracy and reliability of weather predictions. In this paper, we introduce SkySearch, a large-scale satellite video search system deployed at the Korean Meteorological Administration (KMA). SkySearch is designed to aid weather experts in making timely and accurate forecasts by rapidly and precisely searching for satellite videos in the database that resemble current weather conditions. SkySearch employs self-supervised learning to compress large volumes of high-resolution satellite videos into low-dimensional embeddings, addressing the absence of labeled satellite data. Within this latent space, relationships between videos are modeled as a graph, enabling efficient searches. Given a query video, SkySearch rapidly identifies a small subset of similar videos by traversing the graph. When the query video represents the current conditions, it can optionally be augmented with predicted future frames to search for videos that reflect both the current conditions and expected evolution. Finally, the ranked list of videos is provided to weather forecasters through a user-friendly interface. We demonstrate that deployment and empirical effectiveness of SkySearch through both numerical and qualitative evaluations. In summary, SkySearch is: (a) Scalable: processes queries from a large-scale database of satellite images spanning over a decade and delivers results within seconds(b) Accurate: returns numerically and qualitatively similar videos to the query video, and (c) Label-free: does not require labeled videos.
MonoDeMB: Comprehensive Monocular DepthMap Benchmark
Authors: Vaagn Chopuryan, Mikhail Kuznetsov, Vasilii Latonov, Vladimir Mashurov, Natalia Semenova
Venue: KDD
Year: 2025

In this paper, we introduce a comprehensive benchmark for learning-based monocular depth estimation methods. In recent years, depthmap calculation methods have achieved impressive results. The process of comparing the performance of different models requires a lot of resources and time, which can be costly for many developers. The goal of our benchmark is to provide a comparison of how the top models perform on various datasets. We present a table with results based on our tests performed on several popular datasets and one dataset introduced in this paper. In addition, we provide a toolkit that allows each model selected for the benchmark to be tested on any suitable dataset.
Evaluating Decision Rules Across Many Weak Experiments
Authors: Winston Chou, Colin Gray, Nathan Kallus, Aurélien Bibaut, Simon Ejdemyr
Venue: KDD
Year: 2025

Technology firms conduct randomized controlled experiments (''A/B tests'') to learn which actions to take to improve business outcomes. In firms with mature experimentation platforms, experimentation programs can consist of many thousands of tests. To effectively scale experimentation, firms rely on decision rules: standard operating procedures for mapping the results of an experiment to a choice of treatment arm to launch to the general user population. Despite the critical role of decision rules in translating experimentation into business decisions, rigorous guidance on how to evaluate and choose decision rules is scarce. This paper proposes to evaluate decision rules based on their cumulative returns to business north star metrics. Although intuitive and easy to explain to decision-makers, this quantity can be difficult to estimate, especially when experiments have weak signal-to-noise ratios. We develop a cross-validation estimator that is much less biased than the naive plug-in estimator under conditions realistic to digital experimentation. We demonstrate the efficacy of our approach via a case study of 123 historical A/B tests at Netflix, where we used it to show that a new decision rule would have increased cumulative returns to the north star metric by an estimated 33%, directly leading to the adoption of the new rule.
AI for Supply Chain: Today and Future
Authors: Ranak Roy Chowdhury, Yan Liu, Huiming Qu, Qingsong Wen, Chen-Yu Lee, Narendra Agrawal, Alexis Roos
Venue: KDD
Year: 2025

A supply chain is the network of entities and processes involved in the production and distribution of a commodity. Supply chains are a critical backbone across industries like retail, manufacturing, healthcare, and automotive, driving everything from product availability to operational efficiency and customer satisfaction. Modern supply chains are 1) non-cooperative, functioning as fragmented systems where isolated technologies solve individual problems without integration, and 2) unadaptable, failing to adjust to real-time data and uncertainties, like demand fluctuations and regulatory changes. As a result, frequent manual overrides are required, as even small errors can lead to significant financial losses, strained customer relationships, or reputational damage. Modern Artificial Intelligence (AI) advancements offer great potential to unify fragmented supply chains into a seamless, adaptive system while enhancing automation, decision-making, and transparency. In this workshop, we will examine critical supply chain challenges and demonstrate how AI can provide accurate, efficient, and scalable solutions.
Empirical Bayes Selection for Value Maximization
Authors: Dominic Coey, Kenneth Hung
Venue: KDD
Year: 2025

We study the problem of selecting the best m units from a set of n as m / n → α ∈ (0, 1), where noisy, heteroskedastic measurements of the units' true values are available and the decision-maker wishes to maximize the aggregate true value of the units selected. Given a parametric prior distribution, the empirical Bayes decision rule incurs O p(n^-1) regret relative to the Bayesian oracle that knows the true prior. More generally, if the error in the estimated prior is of order O p(rn), regret is Op(rn2). In this sense selection of the best units is fundamentally easier than estimation of their values. We show this regret bound is sharp in the parametric case, by giving an example in which it is attained. Using priors calibrated from a dataset of over four thousand internet experiments, we confirm that empirical Bayes methods perform well in detecting the best treatments with only a modest number of experiments.
Engagement-Driven Content Generation with Large Language Models
Authors: Erica Coppolillo, Federico Cinus, Marco Minici, Francesco Bonchi, Giuseppe Manco
Venue: KDD
Year: 2025

Large Language Models (LLMs) demonstrate significant persuasive capabilities in one-on-one interactions, but their influence within social networks, where interconnected users and complex opinion dynamics pose unique challenges, remains underexplored. This paper addresses the research question: Can LLMs generate meaningful content that maximizes user engagement on social networks? To answer this, we propose a pipeline using reinforcement learning with simulated feedback, where the network's response to LLM-generated content (i.e., the reward) is simulated through a formal engagement model. This approach bypasses the temporal cost and complexity of live experiments, enabling an efficient feedback loop between the LLM and the network under study. It also allows to control over endogenous factors such as the LLM's position within the social network and the distribution of opinions on a given topic. Our approach is adaptive to the opinion distribution of the underlying network and agnostic to the specifics of the engagement model, which is embedded as a plug-and-play component. Such flexibility makes it suitable for more complex engagement tasks and interventions in computational social science. Using our framework, we analyze the performance of LLMs in generating social engagement under different conditions, showcasing their full potential in this task. The experimental code is publicly available at https://github.com/mminici/Engagement-Driven-Content-Generation.
Synthetic Tabular Data: Methods, Attacks and Defenses
Authors: Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
Venue: KDD
Year: 2025

Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this survey, we cover the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learning. We provide background and motivation, before giving a technical deep-dive into the methodologies. We also address the limitations of synthetic data, by studying attacks that seek to retrieve information about the original sensitive data. Finally, we present extensions and open problems in this area.
ArnoldiGCL: Graph Contrastive Learning via Learnable Arnoldi-Based Guided Spectral Chebyshev Polynomial Filters
Authors: Mustafa Coskun, Abdelkader Baggag, Mehmet Koyutürk
Venue: KDD
Year: 2025

Graph Contrastive Learning (GCL) emerged as a powerful paradigm in self-supervised graph representation learning. While earlier applications of GCL rely on homophily assumptions, spectral graph neural networks (GNNs) enhance the effectiveness of GCL on heterophilic graphs by incorporating both low-pass and high-pass filters. However, due to numerical considerations, existing approaches oversimplify low-pass and high-pass filters by modeling them as basic linear operations, failing to capture complex topological relationships. Here, we propose ArnoldiGCL, a novel algorithm that enables the application of complex spectral filters for Graph Contrastive Learning (GCL). Using Arnoldi orthonormalization-based Chebyshev interpolation, ArnoldiGCL overcomes the difficulties posed by ill-conditioned Vandermonde systems that arise in the modeling of complex filters. By introducing learnable filters, our method generates diverse spectral views and effectively captures nuanced graph structures. Theoretical analysis demonstrates that ArnoldiGCL accurately interpolates complex filters, thus forming a solid foundation for contrastive learning on graphs with complex structures. Extensive experiments on real-world datasets confirm that ArnoldiGCL significantly outperforms state-of-the-art GCL algorithms on both homophilic and heterophilic graphs, showcasing its robustness and versatility. The code for ArnoldiGCLis available as open source at: https://github.com/mustafaCoskunAgu/ArnoldiGCL
Mixing Time Matters: Accelerating Effective Resistance Estimation via Bidirectional Method
Authors: Guanyu Cui, Hanzhi Wang, Zhewei Wei
Venue: KDD
Year: 2025

We study the problem of efficiently approximating the effective resistance (ER) on undirected graphs, where ER is a widely used node proximity measure with applications in graph spectral sparsification, multi-class graph clustering, network robustness analysis, graph machine learning, and more. Specifically, given any nodes s and t in an undirected graph G, we aim to efficiently estimate the ER value R(s,t) between nodes s and t, ensuring a small absolute error ε. The previous best algorithm for this problem has a worst-case computational complexity of Õ(Lmax3/ε2d2), where the value of Lmax depends on the mixing time of random walks on G, d = mind(s), d(t), and d(s), d(t) denote the degrees of nodes s and t, respectively. We improve this complexity to Õ ( min Lmax7/3/ε2/3, Lmax3/ε2d2,mLmax ), achieving a theoretical improvement of Õ (maxLmax2/3/ε4/3d2,1, Lmax2/ε2d2m2) over previous results. Here, m denotes the number of edges. Given that Lmax is often very large in real-world networks (e.g., Lmax > 104), our improvement on Lmax is significant, especially for real-world networks. We also conduct extensive experiments on real-world and synthetic graph datasets to empirically demonstrate the superiority of our method. The experimental results show that our method achieves a 10× to 1000× speedup in running time while maintaining the same absolute error compared to baseline methods.
International Workshop on Online and Adaptive Recommender Systems (OARS 2025)
Authors: Xiquan Cui, Derek Zhiyuan Cheng, Fei Liu, Tao Ye, Julian J. McAuley, Vachik S. Dave, Stephen Guo
Venue: KDD
Year: 2025

Recommender system (RecSys) plays important roles in helping users navigate, discover, and consume massive and highly-dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. Therefore, it is important to investigate more on online and adaptive recommender systems (OARS) that can adapt in real time to meet user needs and be robust against distribution shifts. Every year, the organizers survey the most important topics for OARS and propose a new workshop program. In light of the recent advancement of (multi-modal) LLMs in RecSys, in this new edition, we decide to formally add the new topic of (multi-modal) LLM models in OARS. We will invite experts and papers in the field to disseminate new knowledge and foster further advancements.
Cross-Platform Online Team Formation in Spatial Crowdsourcing
Authors: Xiaoxi Cui, Yurong Cheng, Xiangmin Zhou, Yongjiao Sun
Venue: KDD
Year: 2025

Spatial crowdsourcing has become popular in recent years, but traditional tasks focus on one-to-one services with single skills like food delivery and ride-hailing. As societal needs grow more complex, there is a need for tasks requiring teams with multiple skills. Current team formation methods using workers from a single platform limit skill diversity, leading to potential task delays, lower quality, and revenue losses. Although cross-platform cooperation offers a potential solution to skill diversity limitations, it faces two challenges: (1) Data protection regulations mandate that platform's raw data must remain localized; (2) cross-platform cooperation incurs additional cooperation costs. To address these challenges, we first define the Cross-platform Online Team Formation (COTF) problem. We then propose a COTF framework and Random Cooperation Strategy to solve COTF problem. To enhance the effectiveness of cooperation, we further propose Precision Query Range Optimization Strategy (PQROS) for worker selection through adaptive range queries, and Dynamic Query Optimization (DQO) for cost-effective scheduling via predictive revenue modeling. Extensive experiments on real and synthetic datasets validate the effectiveness of our proposed methods.
FlanS: A Foundation Model for Free-Form Language-based Segmentation in Medical Images
Authors: Longchao Da, Rui Wang, Xiaojian Xu, Parminder Bhatia, Taha A. Kass-Hout, Hua Wei, Cao Xiao
Venue: KDD
Year: 2025

Medical imaging is crucial for diagnosing a patient's health condition, and accurate segmentation of these images is essential for isolating regions of interest to ensure precise diagnosis and treatment planning. Existing methods primarily rely on bounding boxes or point-based prompts, while few have explored text-related prompts, despite clinicians often describing their observations and instructions in natural language. To address this gap, we first propose a RAG-based free-form text prompt generator that leverages the domain corpus to generate diverse and realistic descriptions. Then, we introduce FLanS, a novel medical image segmentation model that handles various free-form text prompts, including professional anatomy-informed queries, anatomy-agnostic position-driven queries, and anatomy-agnostic size-driven queries. Additionally, our model also incorporates a symmetry-aware canonicalization module to ensure consistent, accurate segmentations across varying scan orientations and reduce confusion between the anatomical position of an organ and its appearance in the scan. FLanS is trained on a large-scale dataset of over 100k medical images from 7 public datasets. Comprehensive experiments demonstrate the model's superior language understanding and segmentation precision, along with a deep comprehension of the relationship between them, outperforming SOTA baselines on both in-domain and out-of-domain datasets.