OpenRaiser/Intern-Atlas huggingface dataset:
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
Authors: Weichen Zhao, Chenguang Wang, Xinyan Wang, Congying Han, Tiande Guo, Tianshu Yu
Venue: KDD
Year: 2025

This paper presents an analytical study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Generalizing beyond extant approaches grounded in random walk analysis or particle systems, we approach this problem through operator semigroup theory. This theoretical framework allows us to rigorously prove that oversmoothing is intrinsically linked to the ergodicity of the diffusion operator. Relying on semigroup method, we can quantitatively analyze the dynamic of graph diffusion and give a specific mathematical form of the smoothing feature by ergodicity and invariant measure of operator, which improves previous works only show existence of oversmoothing. This finding further poses a general and mild ergodicity-breaking condition, encompassing the various specific solutions previously offered, thereby presenting a more universal and theoretically grounded approach to relieve oversmoothing in diffusion-based GNNs. Additionally, we offer a probabilistic interpretation of our theory, forging a link with prior works and broadening the theoretical horizon. Our experimental results reveal that this ergodicity-breaking term effectively mitigates oversmoothing measured by Dirichlet energy, and simultaneously enhances performance in node classification tasks.
Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Authors: Yige Zhao, Jianxiang Yu, Yao Cheng, Chengcheng Yu, Yiding Liu, Xiang Li, Shuaiqiang Wang
Venue: KDD
Year: 2025

Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently witnessed excellent performance in graph mining. However, most existing heterogeneous graph neural networks (HGNNs) fail to simultaneously handle the problems of missing attributes, inaccurate attributes and scarce node labels, which limits their expressiveness. In this paper, we propose a generative self-supervised model GraMI to address these issues simultaneously. Specifically, GraMI first initializes all the nodes in the graph with a low-dimensional representation matrix. After that, based on the variational graph autoencoder framework, GraMI learns both node-level and attribute-level embeddings in the encoder, which can provide fine-grained semantic information to construct node attributes. In the decoder, GraMI reconstructs both links and attributes. Instead of directly reconstructing raw features for attributed nodes, GraMI generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed. In this way, GraMI can not only complete informative features for non-attributed nodes, but rectify inaccurate ones for attributed nodes. Finally, we conduct extensive experiments to show the superiority of GraMI in tackling HINs with missing and inaccurate attributes. Our code and data can be found here: https://github.com/See-r/GraMI.
4th Workshop on End-End Customer Journey Optimization
Authors: Hongying Zhao, Mert Bay, Zhenyu Zhao, Bradley C. Turnbull, Anbang Xu, Neha Gupta
Venue: KDD
Year: 2025

Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their objectives.
Dynamic Structural Clustering Unleashed: Flexible Similarities, Versatile Updates and for All Parameters
Authors: Zhuowei Zhao, Junhao Gan, Boyu Ruan, Zhifeng Bao, Jianzhong Qi, Sibo Wang
Venue: KDD
Year: 2025

We study structural clustering on graphs in dynamic scenarios, where graphs can be updated by arbitrary insertions or deletions of edges/vertices. Our goal is to efficiently compute structural clustering results under three conditions: 1) for any clustering parameters ε and μ provided on the fly, 2) for arbitrary graph update patterns, and 3) for all typical similarity measurements. To achieve this, we propose an algorithm named VD-STAR that is much simpler yet more efficient than state of the art. With a theoretical guarantee on clustering result's quality, VD-STAR can produce clustering results with up to 99.9% accuracy. Moreover, VD-STAR is easy to implement as it just needs to maintain sorted linked lists and hash tables, making it highly deployable in practice. Most importantly, VD-STAR improves the expected per-update time bound from state-of-the-art O(log2 n), which relies on specific assumption on update pattern, to O(log n) amortized in expectation without any assumption on update pattern. We further design two variants of VD-STAR to enhance its empirical performance. Experimental results show that our algorithms consistently outperform state-of-the-art competitors by up to 9,315 times in update time across nine real datasets, while maintaining similar update time and memory usage.
A Survey on Model Extraction Attacks and Defenses for Large Language Models
Authors: Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
Venue: KDD
Year: 2025

Model extraction attacks pose significant security threats to deployed language models, potentially compromising intellectual property and user privacy. This survey provides a comprehensive taxonomy of LLM-specific extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks. We analyze various attack methodologies including API-based knowledge distillation, direct querying, parameter recovery, and prompt stealing techniques that exploit transformer architectures. We then examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios. We propose specialized metrics for evaluating both attack effectiveness and defense performance, addressing the specific challenges of generative language models. Through our analysis, we identify critical limitations in current approaches and propose promising research directions, including integrated attack methodologies and adaptive defense mechanisms that balance security with model utility. This work serves NLP researchers, ML engineers, and security professionals seeking to protect language models in production environments.
Performative Time-Series Forecasting
Authors: Zhiyuan Zhao, Haoxin Liu, Alexander Rodríguez, B. Aditya Prakash
Venue: KDD
Year: 2025

Time-series forecasting is a critical challenge in various domains and has witnessed substantial progress in recent years. Many real-life scenarios, such as public health, economics, and social applications, involve feedback loops where predictive models can trigger actions that influence the outcome they aim to predict, subsequently altering the target variable's distribution. This phenomenon, known as performativity, introduces the potential for 'self-negating' or 'self-fulfilling' predictions. Despite extensive studies on performativity in classification problems across domains, this phenomenon remains largely unexplored in the context of time-series forecasting from a machine-learning perspective. In this paper, we formalize Performative Time-Series Forecasting (PeTS), addressing the challenge for predictions when performativity-induced distribution shifts are possible. We propose a novel solution to PeTS, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts and subsequently predicts targets. We provide theoretical insights suggesting that FPS potentially leads to reduced generalization error. Extensive experiment results demonstrate that FPS consistently outperforms conventional time-series forecasting and concept drift methods, highlighting its efficacy in handling performativity-induced challenges.
SMA-GNN: A Symbol-Aware Graph Neural Network for Signed Link Prediction in Recommender Systems
Authors: Yumeng Zhao, Hongxiang Lin, Shuo Wen, Junjie Shen, Bei Hua
Venue: KDD
Year: 2025

Recommender Systems (RS) play a critical role in enhancing user experiences across online platforms by modeling user-item interactions as bipartite graphs. Predicting signed links in such graphs remains challenging due to the sparsity and complexity of sign distributions and the limitations of traditional methods like matrix factorization and Graph Convolutional Networks (GCNs), which often fail to capture the intricate local topological and sign-based patterns essential for accurate predictions. To address these challenges, we propose SMA-GNN, a framework specifically designed for signed link prediction in bipartite graphs. SMA-GNN combines Local Subgraph Extraction, Two-Anchor Distance Labeling (TADL), and a Symbol-aware Multi-head Attention Mechanism to enhance predictive capability and interpretability. By extracting a closed local subgraph around the target link, our method captures relevant topological and sign contexts. TADL refines this by assigning unique structural labels to nodes based on their proximity to anchor nodes, encapsulating roles and relationships. The symbol-aware attention mechanism integrates edge sign information into the message-passing process, generating highly discriminative subgraph embeddings. Experiments on benchmark datasets show that SMA-GNN outperforms global embedding methods in prediction accuracy and provides deeper insights into user-item interactions, enabling more precise and personalized recommendations. Our code is avilable at https://github.com/xiaohuzidefeijian/SMAGNN/tree/master
Retrieving Intent-covering Demonstrations for Clarification Generation in Conversational Search Systems
Authors: Ziliang Zhao, Changle Qu, Zhicheng Dou, Haonan Chen, Jiajie Jin
Venue: KDD
Year: 2025

Search clarification is a critical user interface for open-domain conversational Web search, where generating high-quality facets for ambiguous or multi-facet queries significantly guides disambiguation and enhances the user's interaction experience. Recently, in-context learning with Large Language Models (LLMs) has emerged as a promising approach for facet generation by leveraging static or similarity-based demonstrations as prompts. However, existing methods predominantly rely on query similarity, failing to account for the multi-dimensional nature of query intents. This limitation can lead LLMs to generate incorrect or suboptimal facets misaligned with user needs. To address this challenge, we propose an intent-covering framework that improves clarification facet generation by selecting demonstrations that comprehensively cover the diverse intents underlying a given query. Specifically, we first train a generative model with beam search to predict potential intents and construct an intent-document graph to capture their semantic relationships. We then introduce a heuristic greedy algorithm that optimizes demonstration selection by maximizing intent coverage. Furthermore, since the order of demonstrations significantly affects generation quality, we develop a re-ranking model to optimize their sequence for better contextual alignment. Experiments demonstrate the superiority of our approach over strong baselines in various lexical and semantic evaluation metrics. Additionally, we conduct an in-depth analysis of how the number, order, and contextual relevance of demonstrations influence generation performance.
Offline Trajectory Optimization for Offline Reinforcement Learning
Authors: Ziqi Zhao, Zhaochun Ren, Liu Yang, Yunsen Liang, Fajie Yuan, Pengjie Ren, Zhumin Chen, Jun Ma, Xin Xin
Venue: KDD
Year: 2025

Offline reinforcement learning (RL) aims to learn policies without online explorations. To enlarge the training data, model-based offline RL learns a dynamics model which is utilized as a virtual environment to generate simulation data and enhance policy learning. However, existing data augmentation methods for offline RL suffer from (i) trivial improvement from short-horizon simulation; and (ii) the lack of evaluation and correction for generated data, leading to low-qualified augmentation. In this paper, we propose offline trajectory optimization for offline reinforcement learning (OTTO). The key motivation is to conduct long-horizon simulation and then utilize model uncertainty to evaluate and correct the augmented data. Specifically, we propose an ensemble of Transformers, a.k.a. World Transformers, to predict environment state dynamics and the reward function. Three strategies are proposed to use World Transformers to generate long-horizon trajectory simulation by perturbing the actions in the offline data. Then, an uncertainty-based World Evaluator is introduced to firstly evaluate the confidence of the generated trajectories and then perform the correction for low-confidence data. Finally, we jointly use the original data with the corrected augmentation data to train an offline RL algorithm. OTTO serves as a plug-in module and can be integrated with existing model-free offline RL methods. Experiments on various benchmarks show that OTTO can effectively improve the performance of representative offline RL algorithms, including in complex environments with sparse rewards like AntMaze. Codes are available at https://github.com/ZiqiZhao1/OTTO
Graph Contrastive Learning with Progressive Augmentations
Authors: Yuhai Zhao, Yejiang Wang, Zhengkui Wang, Wen Shan, Miaomiao Huang, Xingwei Wang
Venue: KDD
Year: 2025

To be still yet still moving. - Do Hyun Choe Graph contrastive learning (GCL) has recently gained prominence in unsupervised graph representation learning. Traditional GCL approaches generally focus on creating a single contrastive view alongside the main graph view, targeting invariant representation learning in a static framework. Our study introduces a novel manner: despite using static graphs, we aim to learn invariant representations by generating a series of evolving contrastive views with temporal coherence and multi-viewpoint insights at various granularities. In this context, we propose the Progressive Augmentation framework for Graph Contrastive Learning (PaGCL). This framework advances beyond traditional methods by producing a sequence of augmented views, each evolving from the previous one, and assigning timestamps based on piecewise smoothness. This approach enables our model to more effectively extract invariant features from these dynamic views, capturing multi-grained structural and temporal information. Our experiments on diverse benchmark datasets demonstrate that PaGCL significantly outperforms current state-of-the-art methods.
Stable Representation Learning on Graphs from Multiple Environments with Structure Distribution Shift
Authors: Tong Zhao, Daixin Wang, Zhiqiang Zhang, Yulin Kang, Jun Zhou
Venue: KDD
Year: 2025

In recent years, Graph Neural Networks (GNNs) become very effective methods to utilize graphs and have been applied to many real-world applications, including recommendation, advertisement, and financial fraud detection. In fact, GNNs are mostly trained and test in the environments with the same distribution. However, in the real cases, selection bias are inevitably existed in both the node features and the graph structures, which will lead to serious impact on the GNN performance. Several works of literature have investigated the out-of-distribution (OOD) problem on the feature distribution, but little research specifically studies the effect caused by the bias of graph structure. However, graph structure is very fundamental for GNNs since it greatly affects the message propagation mechanism. In order to solve the above problem, we propose an unsupervised Stable Graph Representation learning (SGR) framework to obtain stable graphs from multiple environments with graph structure bias, and to improve the stability ability of GNN model across environments. Comprehensive experiments have been carried out on 4 public benchmark dataset and a real-world financial dataset. The experimental results show that the proposed stable learning method significantly improves the stability of GNN model in varying test environments.
Discrepancy-Aware Graph Mask Auto-Encoder
Authors: Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Weigang Lu
Venue: KDD
Year: 2025

Masked Graph Auto-Encoder, a powerful graph self-supervised training paradigm, has recently shown superior performance in graph representation learning. Existing works typically rely on node contextual information to recover the masked information. However, they fail to generalize well to heterophilic graphs where connected nodes may be not similar, because they focus only on capturing the neighborhood information and ignoring the discrepancy information between different nodes, resulting in indistinguishable node representations. In this paper, to address this issue, we propose a Discrepancy-Aware Graph Mask Auto-Encoder (DGMAE). It obtains more distinguishable node representations by reconstructing the discrepancy information of neighboring nodes during the masking process. We conduct extensive experiments on 17 widely-used benchmark datasets. The results show that our DGMAE can effectively preserve the discrepancies of nodes in low-dimensional space. Moreover, DGMAE significantly outperforms state-of-the-art graph self-supervised learning methods on three graph analytic including tasks node classification, node clustering, and graph classification, demonstrating its remarkable superiority. The code of DGMAE is available at https://github.com/zhengziyu77/DGMAE.
Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs
Authors: Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Weigang Lu
Venue: KDD
Year: 2025

Real-world networks usually have a property of node heterophily, that is, the connected nodes usually have different features or different labels. This heterophily issue has been extensively studied in homogeneous graphs but remains under-explored in heterogeneous graphs, where there are multiple types of nodes and edges. Capturing node heterophily in heterogeneous graphs is very challenging since both node/edge heterogeneity and node heterophily should be carefully taken into consideration. Existing methods typically convert heterogeneous graphs into homogeneous ones to learn node heterophily, which will inevitably lose the potential heterophily conveyed by heterogeneous relations. To bridge this gap, we propose Relation-Aware Separation of Homophily and Heterophily (RASH), a novel contrastive learning framework that explicitly models high-order semantics of heterogeneous interactions and adaptively separates homophilic and heterophilic patterns. Particularly, RASH introduces dual heterogeneous hypergraphs to encode multi-relational bipartite subgraphs and dynamically constructs homophilic graphs and heterophilic graphs based on relation importance. A multi-relation contrastive loss is designed to align heterogeneous and homophilic/heterophilic views by maximizing mutual information. In this way, RASH simultaneously resolves the challenges of heterogeneity and heterophily in heterogeneous graphs. Extensive experiments on benchmark datasets demonstrate the effectiveness of RASH across various downstream tasks. The code is available at: https://github.com/zhengziyu77/RASH.
Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment
Authors: Liangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
Venue: KDD
Year: 2025

Large Language Models (LLMs) have demonstrated impressive performance in time series analysis and seems to understand the time temporal relationship well than traditional transformer-based approaches. However, since LLMs are not designed for time series tasks, simpler models-like linear regressions can often achieve comparable performance with far less complexity. In this study, we perform extensive experiments to assess the effectiveness of applying LLMs to key time series tasks, including forecasting, classification, imputation, and anomaly detection. We compare the performance of LLMs against simpler baseline models, such as single-layer linear models and randomly initialized LLMs. Our results reveal that LLMs offer minimal advantages for these core time series tasks and may even distort the temporal structure of the data. In contrast, simpler models consistently outperform LLMs while requiring far fewer parameters. Furthermore, we analyze existing reprogramming techniques and show, through data manifold analysis, that these methods fail to effectively align time series data with language and display ''pseudo-alignment'' behavior in embedding space. Our findings suggest that the performance of LLM-based methods in time series tasks arises from the intrinsic characteristics and structure of time series data, rather than any meaningful alignment with the language model architecture. We release the code for experiments here: https://github.com/IcurasLW/Official-Repository_Understanding_LLM_for_Time_Series_Analysis.git
Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation
Authors: Yihang Zheng, Bo Li, Zhenghao Lin, Yi Luo, Xuanhe Zhou, Chen Lin, Guoliang Li, Jinsong Su
Venue: KDD
Year: 2025

The development of Large Language Models (LLMs) has revolutionized QA across various industries, including the database domain. However, there lacks a thorough evaluation regarding the capabilities of different LLMs in database QA. To this end, we introduce DQABench, the first comprehensive database QA benchmark for LLMs. DQABench features an innovative LLM-based method to automate the generation, cleaning, and rewriting of evaluation dataset, resulting in over 200,000 QA pairs in English and Chinese. These QA pairs cover a wide range of database-specific knowledge extracted from manuals, online communities, and DB instances, allowing for assessment of LLMs' Retrieval-Augmented Generation (RAG) and Tool Invocation Generation (TIG) capabilities in the database QA task. Furthermore, we propose a highly modular and scalable testbed DQATestbed, with basic and advanced components such as Fine-tuning, Question Classification Routing (QCR), RAG, TIG, and Prompt Template Engineering (PTE). Finally, we provide an evaluation pipeline that computes various metrics throughout a standardized evaluation process to ensure the accuracy and fairness. Our evaluation reveals the strengths and limitations of nine open-source and commercial LLMs, and the impact of various service components (e.g., fine-tuning, QCR, RAG, TIG). The proposed benchmark dataset is available at https://github.com/XMUDM/DQABench.