OpenRaiser/Intern-Atlas huggingface dataset:
ScIRGen: Synthesize Realistic and Large-Scale RAG Dataset for Scientific Research
Authors: Junyong Lin, Lu Dai, Ruiqian Han, Yijie Sui, Ruilin Wang, Xingliang Sun, Qinglin Wu, Min Feng, Hao Liu, Hui Xiong
Venue: KDD
Year: 2025

Scientific researchers need intensive information about datasets to effectively evaluate and develop theories and methodologies. The information needs regarding datasets are implicitly embedded in particular research tasks, rather than explicitly expressed in search queries. However, existing scientific retrieval and question-answering (QA) datasets typically address straightforward questions, which do not align with the distribution of real-world research inquiries. To bridge this gap, we developed ScIRGen, a dataset generation framework for scientific QA & retrieval that more accurately reflects the information needs of professional science researchers, and uses it to create a large-scale scientific retrieval-augmented generation (RAG) dataset with realistic queries, datasets and papers. Technically, we designed a dataset-oriented information extraction method that leverages academic papers to augment the dataset representation. We then proposed a question generation framework by employing cognitive taxonomy to ensure the quality of synthesized questions. We also design a method to automatically filter synthetic answers based on the perplexity shift of LLMs, which is highly aligned with human judgment of answers' validity. Collectively, these methodologies culminated in the creation of the 61k QA dataset, ScIRGen-Geo. We benchmarked representative methods on the ScIRGen-Geo dataset for their question-answering and retrieval capabilities, finding out that current methods still suffer from reasoning from complex questions. This work advances the development of more sophisticated tools to support the intricate information needs of the scientific community.
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
Authors: Junhong Lin, Xiaojie Guo, Shuaicheng Zhang, Yada Zhu, Julian Shun
Venue: KDD
Year: 2025

Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node and edge types exist in the graph, and heterophily, where connected nodes may have dissimilar labels and attributes. However, existing benchmarks primarily focus on either heterophilic homogeneous graphs or homophilic heterogeneous graphs, leaving a significant gap in understanding how models perform on graphs with both heterogeneity and heterophily. To bridge this gap, we introduce H2GB, a large-scale node-classification graph benchmark that brings together the complexities of both the heterophily and heterogeneity properties of real-world graphs. H2GB encompasses 9 real-world datasets spanning 5 diverse domains, 28 baseline models, and a unified benchmarking library with a standardized data loader, evaluator, unified modeling framework, and an extensible framework for reproducibility. We establish a standardized workflow supporting both model selection and development, enabling researchers to easily benchmark graph learning methods. Extensive experiments across 28 baselines reveal that current methods struggle with heterophilic and heterogeneous graphs, underscoring the need for improved approaches. Finally, we present a new variant of the model, H2G-former, developed following our standardized workflow, that excels at this challenging benchmark. Both the benchmark and the framework are publicly available at Github and PyPI, with documentation hosted at https://junhongmit.github.io/H2GB.
Diffusion Models for Recommender Systems: From Content Distribution To Content Creation
Authors: Jianghao Lin, Yang Cao, Yong Yu, Weinan Zhang
Venue: KDD
Year: 2025

Recommender systems (RSs) have become essential for alleviating information overload and matching users with relevant content. Traditionally, RSs have focused on personalized content distribution, leveraging user interaction data and various features to rank and recommend existing items. Recently, diffusion models (DMs) have emerged as powerful generative paradigms, introducing new possibilities for RSs to not only enhance their performance for content distribution but also extend their capability boundaries to personalized content creation. On the one hand, DMs enhance the recommendation performance by mitigating challenges such as sparse user-item interactions, weak latent representations, and noisy data. On the other hand, DMs enable personalized content creation, transforming RSs from passive distributors into active generators of user-specific media assets, such as customized images, posters, and multimedia content. Given such a transformative paradigm shift, this survey provides a comprehensive review of the integration of diffusion models into recommender systems, exploring key methodologies, application scenarios, and their impact on recommendation effectiveness, diversity, and personalization. We categorize DM-based recommendation paradigms into content distribution and content creation, compare integration strategies, and discuss open challenges and future directions. By bridging the gap between diffusion models and recommender systems, this work aims to guide researchers and practitioners in developing the next generation of generative AI-powered recommendation solutions.
Robust Data-Driven Auction Design
Authors: Qilong Lin, Yangsu Liu, Dagui Chen, Zhenzhe Zheng, Jian Xu, Bo Zheng, Fan Wu, Guihai Chen
Venue: KDD
Year: 2025

In the field of auction design, leveraging deep learning to solve optimal auctions from sampled data has become a promising direction. However, real-world contexts often involve uncertain data, which would severely affect the auction performance, but it is lacking consideration in existing works. To address this challenge, we incorporate these uncertainties into auction design metrics, and frame this challenge as a robust data-driven auction design problem. To solve this problem, we first propose the GAT method, where we introduce the process of problem relaxation and transformation to address the non-differentiable variable presented in the original problem, and further propose an adversarial training algorithm to solve the mini-max problem after transformation. Moreover, to obtain moderately robust auctions, we propose two methods to select the robust coefficient, which provides guidance and insights for selecting robust auctions based on generalization and performance metrics. Finally, with the insights from the GAT method, we further propose the SAT method, where we employ a strict and unified IC constraint that extends from the GAT method, which provides strong IC guarantees and stable revenue in uncertain environments. Experiments on both constructed and real-world datasets show that our robust methods effectively improve the performance of auctions in terms of revenue and IC guarantees.
Efficient Bitruss Decomposition without Butterfly Enumeration
Authors: Fengnian Lin, Boyu Ruan, Junhao Gan, Lei Li
Venue: KDD
Year: 2025

Mining cohesive subgraphs in bipartite graphs is of great importance to various real-world applications such as recommendation in e-commercial systems and fraud detections in social networks. In this paper, we study the problem of Bitruss Decomposition of a given bipartite graph G. The goal is to compute, for each edge e, the largest value of k such that e is still contained in a k-bitruss. Here, a k-bitruss is a maximal subgraph of G such that every edge of it is contained in at least k butterflies (i.e(2,2)-cliques) in the subgraph. All the previous state-of-the-art solutions are based on the well-known Peeling Process framework and have to ''touch'' every butterfly in the graph G for at least once, causing a O(⧖G) cost, where ⧖G is the number of butterflies in G. In the worst case, ⧖G can be as large as O(m2), where m is the number of edges in G. We propose the first algorithm called BiT-DT, whose running time is bounded by O(m^1.5 log m), significantly improving the state-of-the-art bound by roughly a factor of O(√m). The crucial idea of our algorithm is an efficient approach to identify the edges to be peeled next in the Peeling Process without maintaining a precise butterfly support for each edge. To further enhance the practical performance of our BiT-DT algorithm, we propose a heuristic to peel the edges in batch. Extensive experimental results on ten real-world datasets show that our proposed algorithm outperforms the state-of-the-art baselines by up to an order of magnitude in terms of running time.
Spectral Subspace Clustering for Attributed Graphs
Authors: Xiaoyang Lin, Renchi Yang, Haoran Zheng, Xiangyu Ke
Venue: KDD
Year: 2025

Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k« n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitioning vertices in attributed graphs, referred to as SCAG. However, these works either demand significant computational overhead for constructing the nxn self-expressive matrix, or fail to incorporate graph topology and attribute data into the subspace clustering framework effectively, and thus, compromise result quality. Motivated by this, this paper presents two effective and efficient algorithms, S2CAG M-S2CAG for SCAG computation. Particularly, S2CAG obtains superb performance through three major contributions. First, we formulate a new objective function for SCAG with a refined representation model for vertices and two non-trivial constraints. On top of that, an efficient linear-time optimization solver is developed based on our theoretically grounded problem transformation and well-thought-out adaptive strategy. We then conduct an in-depth analysis to disclose the theoretical connection of S2CAG to conductance minimization, which further inspires the design of M-S2CAG that maximizes the modularity. Our extensive experiments, comparing S2CAG and M-S2CAG against 17 competitors over 8 benchmark datasets, exhibit that our solutions outperform all baselines in terms of clustering quality measured against the ground truth while delivering high efficiency.
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
Authors: Minhua Lin, Zhiwei Zhang, Enyan Dai, Zongyu Wu, Yilong Wang, Xiang Zhang, Suhang Wang
Venue: KDD
Year: 2025

Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning of the entire model. Despite the advantages of GPL, little attention has been given to its vulnerability to backdoor attacks, where an adversary can manipulate the model's behavior by embedding hidden triggers. Existing graph backdoor attacks rely on modifying model parameters during training, but this approach is impractical in GPL as GNN encoder parameters are frozen after pre-training. Moreover, downstream users may fine-tune their own task models on clean datasets, further complicating the attack. In this paper, we propose TGPA, a backdoor attack framework designed specifically for GPL. TGPA injects backdoors into graph prompts without modifying pre-trained GNN encoders and ensures high attack success rates and clean accuracy. To address the challenge of model fine-tuning by users, we introduce a finetuning-resistant poisoning approach that maintains the effectiveness of the backdoor even after downstream model adjustments. Extensive experiments on multiple datasets under various settings demonstrate the effectiveness of TGPA in compromising GPL models with fixed GNN encoders.
A Fraudulent Blind Shipment Detection Framework in Logistics
Authors: Hongyu Lin, Shuxin Zhong, Yan Fang, Zhiqing Hong, Wenjun Lyu, Qipeng Xie, Haotian Wang, Lu Wang, Kaishun Wu
Venue: KDD
Year: 2025

An emerging type of fraud involves malicious senders exploiting the blind shipment and cash-on-delivery (COD) mechanisms by dispatching large volumes of unsolicited, low-cost parcels. If unsuspecting receivers accept these parcels, they pay for both shipping and goods; otherwise, logistics providers bear the round-trip shipping costs. Existing detection techniques, which rely on extensive labeled cases, struggle with this emerging fraud because receivers' unawareness and low transaction values discourage complaints, resulting in few confirmed cases. Therefore, we propose leveraging receivers' complaints, though not initially collected for fraud detection, to uncover subtle indicators of fraud patterns, while addressing three challenges: (C1) noise-rich dialogues(C2) data privacy concerns, and (C3) ever-evolving fraud patterns. To address them, we design BLOFF, a Blind shipment detection Framework for LO gistics Fraud powered by large language models (LLMs). Specifically, BLOFF includes three components: i) Sensitivity Anonymization to protect sensitive user information; ii) Dialogue Profile Distillation to transform informal dialogues into structured representation, addressing C1, and distill knowledge from a teacher LLM (GPT-4o) to a lightweight student LLM (ChatGLM4-9B), addressing C2; ii) Multi-faceted Context Augmentation to enhance the interpretation of fraud signatures and adaptation of evolving patterns, addressing C3. We evaluate BLOFF on about 56,000 complaints records collected from JD Logistics between January and November 2024. Results show that BLOFF outperforms state-of-the-art methods, achieving a 10.19% improvement in precision. Furthermore, during its real-world deployment in December 2024, BLOFF identified over 90 fraudulent parcels with a 91.4% precision.
KDD Workshop on Evaluation and Trustworthiness of Agentic and Generative AI
Authors: Yuan Ling, Shujing Dong, Zheng Chen, Yarong Feng, Sadid Hasan, George Karypis, Chandan K. Reddy
Venue: KDD
Year: 2025

The rapid deployment of Generative and Agentic AI systems-ranging from large language models to autonomous agents-has created a critical need for rigorous and trustworthy evaluation methodologies. As these models influence real-world decision-making, traditional performance metrics alone fall short in capturing issues of safety, ethical alignment, misinformation, and human-centered usability. This workshop addresses these challenges by fostering interdisciplinary discussions and innovations in evaluation strategies that go beyond conventional benchmarks. Topics include holistic and multi-perspective assessments, scalable evaluation pipelines, reasoning and goal alignment in agentic behavior, misinformation detection, cross-modal generation, and trust calibration. By advancing robust, user-centric, and societally grounded evaluation practices, this workshop contributes to expanding KDD's methodological frontier into the emerging domain of responsible AI systems.
Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search
Authors: Rui Liu, Rui Xie, Zijun Yao, Yanjie Fu, Dongjie Wang
Venue: KDD
Year: 2025

Feature selection removes redundant features to enhance both performance and computational efficiency in downstream tasks. Existing methods often struggle to capture complex feature interactions and adapt to diverse scenarios. Recent advances in this domain have incorporated generative intelligence to address these drawbacks by uncovering intricate relationships between features. However, two key limitations remain: 1) embedding feature subsets in a continuous space is challenging due to permutation sensitivity, as changes in feature order can introduce biases and weaken the embedding learning process; 2) gradient-based search in the embedding space assumes convexity, which is rarely guaranteed, leading to reduced search effectiveness and suboptimal subsets. To address these limitations, we propose a new framework that can: 1) preserve feature subset knowledge in a continuous embedding space while ensuring permutation invariance; 2) effectively explore the embedding space without relying on strong convex assumptions. For the first objective, we develop an encoder-decoder paradigm to preserve feature selection knowledge into a continuous embedding space. This paradigm captures feature interactions through pairwise relationships within the subset, removing the influence of feature order on the embedding. Moreover, an inducing point mechanism is introduced to accelerate pairwise relationship computations. For the second objective, we employ a policy-based reinforcement learning (RL) approach to guide the exploration of the embedding space. The RL agent effectively navigates the space by balancing multiple objectives. By prioritizing high-potential regions adaptively and eliminating the reliance on convexity assumptions, this search strategy effectively reduces the risk of converging to local optima. Finally, we conduct extensive experiments to demonstrate the effectiveness, efficiency, robustness and explicitness of our model. Our code and dataset are publicly accessible on GitHub. https://github.com/RayLiu1103/CAPS.
Data Heterogeneity Modeling for Trustworthy Machine Learning
Authors: Jiashuo Liu, Peng Cui
Venue: KDD
Year: 2025

Data heterogeneity plays a pivotal role in determining the performance of machine learning (ML) systems. Traditional algorithms, which are typically designed to optimize average performance, often overlook the intrinsic diversity within datasets. This oversight can lead to a myriad of issues, including unreliable decision-making, inadequate generalization across different domains, unfair outcomes, and false scientific inferences. Hence, a nuanced approach to modeling data heterogeneity is essential for the development of dependable, data-driven systems. In this survey paper, we present a thorough exploration of heterogeneity-aware machine learning, a paradigm that systematically integrates considerations of data heterogeneity throughout the entire ML pipeline-from data collection and model training to model evaluation and deployment. By applying this approach to a variety of critical fields, including healthcare, agriculture, finance, and recommendation systems, we demonstrate the substantial benefits and potential of heterogeneity-aware ML. These applications underscore how a deeper understanding of data diversity can enhance model robustness, fairness, and reliability and help model diagnosis and improvements. Moreover, we delve into future directions and provide research opportunities for the whole data mining community, aiming to promote the development of heterogeneity-aware ML.
Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits
Authors: Maoli Liu, Zhuohua Li, Xiangxiang Dai, John C. S. Lui
Venue: KDD
Year: 2025

Conversational recommender systems proactively query users with relevant ''key terms'' and leverage the feedback to elicit users' preferences for personalized recommendations. Conversational contextual bandits, a prevalent approach in this domain, aim to optimize preference learning by balancing exploitation and exploration. However, several limitations hinder their effectiveness in real-world scenarios. First, existing algorithms employ key term selection strategies with insufficient exploration, often failing to thoroughly probe users' preferences and resulting in suboptimal preference estimation. Second, current algorithms typically rely on deterministic rules to initiate conversations, causing unnecessary interactions when preferences are well-understood and missed opportunities when preferences are uncertain. To address these limitations, we propose three novel algorithms: CLiSK, CLiME, and CLiSK-ME. CLiSK introduces smoothed key term contexts to enhance exploration in preference learning, CLiME adaptively initiates conversations based on preference uncertainty, and CLiSK-ME integrates both techniques. We theoretically prove that all three algorithms achieve a tighter regret upper bound of O (√dTlogT) with respect to the time horizon T, improving upon existing methods. Additionally, we provide a matching lower bound Ω(√dT) for conversational bandits, demonstrating that our algorithms are nearly minimax optimal. Extensive evaluations on both synthetic and real-world datasets show that our approaches achieve at least a 14.6% improvement in cumulative regret.
MOTTO: A Mixture-of-Experts Framework for Multi-Treatment, Multi-Outcome Treatment Effect Estimation
Authors: Yiling Liu, Wei Shi, Chen Fu, Ziyang Jiang, Zhigang Hua, David E. Carlson
Venue: KDD
Year: 2025

Multi-treatment multi-outcome treatment effect estimation plays a vital role in today's industry-level applications. For example, in social media ads, practitioners simultaneously deploy multiple interventions to users' experience and track multi-faceted metrics (e.g., ad performance, engagement, churn). However, existing methods for estimating treatment effects struggle to simultaneously address the complex interplays and ensure robust counterfactual balancing across treatment-outcome pairs. In our paper, we propose MOTTO (Multi-Outcome, Multi-Treat-ment Transfer Model for Treatment Effect Estimation), a Mixture-of-Experts-based framework designed to jointly handle these multi-faceted challenges. MOTTO explicitly learns the relationships across outcomes and distribution overlaps across treatment groups by isolating shared experts within its partitioned architecture. It then employs selective distribution alignment to these treatment-shared experts, optimizing the balance between factual and counterfactual predictions. Importantly, MOTTO can scale to a large number of treatments and outcomes without significantly increasing the number of parameters, as its shared experts can be primarily used for efficiency with comparable performance. We validate MOTTO on synthetic data, demonstrating MOTTO's strong adaptability to varying levels of outcome correlations and confounding. We further highlight its performance improvements across real-world benchmarks and a large-scale advertising ecosystem on one of the world's largest social media platforms.
Training Industry-scale GNNs with GiGL
Authors: Yozen Liu, Tong Zhao, Matthew Kolodner, Kyle Montemayor, Shubham Vij, Neil Shah
Venue: KDD
Year: 2025

Recent advances in graph machine learning (GML) and Graph Neu- ral Networks (GNNs) have sparked significant practical interest given the ability to model complex relationships between entities. Despite rapid progress in GNN designs, scalability remains a major challenge. Industry applications require solutions that can handle graphs with billions of nodes and edges efficiently. GiGL (Gigantic Graph Learning) is an open-source library from Snapchat, designed for large-scale distributed training and inference with GNNs. It seamlessly integrates with popular open-source GNN libraries like PyTorch Geometric (PyG). GiGL provides simplified configurable interfaces with minimal modeling code requirements, providing in- dustrial practitioners a straightforward way to apply GNNs to large- scale applications and enabling academics to conduct large-scale experiments. At the same time, it enables complex modeling capabil- ities desirable for modeling iteration. In this hands-on tutorial, we will demonstrate how GiGL addresses the scalability challenge in GNNs and provide a step-by-step guide for attendees to complete end-to-end training and inference with GiGL on industry-scale graphs. By the end of our tutorial, participants will have hands-on experience in training GNNs on graphs with billions of nodes and edges - capabilities not easily achievable with open-source graph learning libraries like PyG alone. We anticipate strong interest and participation from both industrial practitioners working on GNN applications and academics conducting large-scale experiments.