OpenRaiser/Intern-Atlas huggingface dataset:
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection
Authors: Xiping Li, Xiangyu Dong, Xingyi Zhang, Kun Xie, Yuanhao Feng, Bo Wang, Guilin Li, Wuxiong Zeng, Xiujun Shu, Sibo Wang
Venue: KDD
Year: 2025

Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neural Network (GNN) methods primarily focus on homogeneous GAD and thus fail to address three key issues: (C1) Capturing abnormal signal and rich semantics across diverse meta-paths; (C2) Retaining high-frequency content in HIN dimension alignment; and (C3) Learning effectively from difficult anomaly samples with class imbalance. To overcome these, we propose ChiGAD, a spectral GNN framework based on a novel Chi-Square filter, inspired by the wavelet effectiveness in diverse domains. Specifically, ChiGAD consists of: (1) Multi-Graph Chi-Square Filter, which captures anomalous information via applying dedicated Chi-Square filters to each meta-path graph; (2) Interactive Meta-Graph Convolution, which aligns features while preserving high-frequency information and incorporates heterogeneous messages by a unified Chi-Square Filter; and (3) Contribution-Informed Cross-Entropy Loss, which prioritizes difficult anomalies to address class imbalance. Extensive experiments on public and industrial datasets show that ChiGAD outperforms state-of-the-art models on multiple metrics. Additionally, its homogeneous variant, ChiGNN, excels on seven GAD datasets, validating the effectiveness of Chi-Square filters. Our code is available at https://github.com/HsipingLi/ChiGAD.
LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Authors: Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo
Venue: KDD
Year: 2025

Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-based KGRSs, Self-Supervised Learning (SSL) has been incorporated to address the sparity issue, leading to longer training time. However, through extensive experiments, we reveal that: (1)compared to other KGRSs, the existing GNN-based KGRSs fail to keep their superior performance under sparse interactions even with SSL. (2) More complex models tend to perform worse in sparse interaction scenarios and complex mechanisms, like attention mechanism, can be detrimental as they often increase learning difficulty. Inspired by these findings, we propose LightKG, a simple yet powerful GNN-based KGRS to address sparsity issues. LightKG includes a simplified GNN layer that encodes directed relations as scalar pairs rather than dense embeddings and employs a linear aggregation framework, greatly reducing the complexity of GNNs. Additionally, LightKG incorporates an efficient contrastive layer to implement SSL. It directly minimizes the node similarity in original graph, avoiding the time-consuming subgraph generation and comparison required in previous SSL methods. Experiments on four benchmark datasets show that LightKG outperforms 12 competitive KGRSs in both sparse and dense scenarios while significantly reducing training time. Specifically, it surpasses the best baselines by an average of 5.8% in recommendation accuracy and saves 84.3% of training time compared to KGRSs with SSL. Our code is available at https://github.com/1371149/LightKG.
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
Authors: Honghao Li, Yiwen Zhang, Yi Zhang, Lei Sang, Jieming Zhu
Venue: KDD
Year: 2025

Hadamard Product (HP) has long been a cornerstone in click-through rate (CTR) prediction tasks due to its simplicity, effectiveness, and ability to capture feature interactions without additional parameters. However, the underlying reasons for its effectiveness remain unclear. In this paper, we revisit HP from the perspective of Quadratic Neural Networks (QNN), which leverage quadratic interaction terms to model complex feature relationships. We further reveal QNN's ability to expand the feature space and provide smooth nonlinear approximations without relying on activation functions. Meanwhile, we find that traditional post-activation does not further improve the performance of the QNN. Instead, mid-activation is a more suitable alternative. Through theoretical analysis and empirical evaluation of 25 QNN neuron formats, we identify a good-performing variant and make further enhancements on it. Specifically, we propose the Multi-Head Khatri-Rao Product as a superior alternative to HP and a Self-Ensemble Loss with dynamic ensemble capability within the same network to enhance computational efficiency and performance. Ultimately, we propose a novel neuron format, QNN-alpha, which is tailored for CTR prediction tasks. Experimental results show that QNN-alpha achieves new state-of-the-art performance on six public datasets while maintaining low inference latency, good scalability, and excellent compatibility. The code, running logs, and detailed hyperparameter configurations are available at: https://github.com/salmon1802/QNN.
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
Authors: Guoming Li, Jian Yang, Yifan Chen
Venue: KDD
Year: 2025

Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various graph-related tasks. Conventional methods adopt a graph-wise filtering paradigm, imposing a uniform filter across all nodes, yet recent findings suggest that this rigid paradigm struggles with heterophilic graphs. To overcome this, recent works have introduced node-wise filtering, which assigns distinct filters to individual nodes, offering enhanced adaptability. However, a fundamental gap remains: a comprehensive framework unifying these two strategies is still absent, limiting theoretical insights into the filtering paradigms. Moreover, through the lens of Contextual Stochastic Block Model, we reveal that a synthesis of graph-wise and node-wise filtering provides a sufficient solution for classification on graphs exhibiting both homophily and heterophily, suggesting the risk of excessive parameterization and potential overfitting with node-wise filtering. To address the limitations, this paper introduces Coarsening-guided Partition-wise Filtering (CPF). CPF innovates by performing filtering on node partitions. The method begins with structure-aware partition-wise filtering, which filters node partitions obtained via graph coarsening algorithms, and then performs feature-aware partition-wise filtering, refining node embeddings via filtering on clusters produced by k-means clustering over features. In-depth analysis is conducted for each phase of CPF, showing its superiority over other paradigms. Finally, benchmark node classification experiments, along with a real-world graph anomaly detection application, validate CPF's efficacy and practical utility. Code is available with the Github repository: https://github.com/vasile-paskardlgm/CPF.
LiPM: Foundation Model for Lithium-Ion Battery Analysis
Authors: Juren Li, Yang Yang, Hanchen Su, Jiayu Liu, Youmin Chen, Jianfeng Zhang, Lujia Pan
Venue: KDD
Year: 2025

Current deep learning approaches for lithium-ion battery analysis are often specialized and limited to specific battery types or individual tasks. While recent advances in large language models (LLMs) highlight the potential of pretraining paradigms, existing time-series pretraining models inadequately address the physicochemical complexity and temporal irregularity inherent to battery operational data. We propose LiPM, a pretrained foundation model that unifies multi-dataset learning through physics-aware objectives and irregularity-tolerant temporal modeling. LiPM introduces three key innovations: (1) A Mix-Masked Autoencoder (MMAE) enforcing electrochemical consistency via joint reconstruction of temporally masked patches and cross-channel masked variables(2) A Coulombic Integration Regression (CIR) task explicitly encoding charge conservation laws, and (3) A dual-scale temporal encoder combining irregular intra-patch processing (preserving raw timestamps) with regular inter-patch attention (capturing macroscopic dynamics). Trained on eight heterogeneous battery datasets without cycle-label annotations, LiPM demonstrates universal applicability across partial charge-discharge segments and irregular sampling protocols. Extensive experiments show remarkable improvements over 9 state-of-the-art baselines in critical downstream tasks.
Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment
Authors: Yibo Li, Yuan Fang, Mengmei Zhang, Chuan Shi
Venue: KDD
Year: 2025

Understanding molecular structure and related knowledge is crucial for scientific research. Recent studies integrate molecular graphs with their textual descriptions to enhance molecular representation learning. However, they focus on the whole molecular graph and neglect frequently occurring subgraphs, known as motifs, which are essential for determining molecular properties. Without such fine-grained knowledge, these models struggle to generalize to unseen molecules and tasks that require motif-level insights. To bridge this gap, we propose FineMolTex, a novel Fine-grained Molecular graph-Text pre-training framework to jointly learn coarse-grained molecule-level knowledge and fine-grained motif-level knowledge. Specifically, FineMolTex consists of two pre-training tasks: a contrastive alignment task for coarse-grained matching and a masked multi-modal modeling task for fine-grained matching. In particular, the latter predicts the labels of masked motifs and words, which are selected based on their importance. By leveraging insights from both modalities, FineMolTex is able to understand the fine-grained matching between motifs and words. Finally, we conduct extensive experiments across three downstream tasks, achieving up to 230% improvement in the text-based molecule editing task. Additionally, our case studies reveal that FineMolTex successfully captures fine-grained knowledge, potentially offering valuable insights for drug discovery and catalyst design.
Causal-aware Graph Neural Architecture Search under Distribution Shifts
Authors: Peiwen Li, Xin Wang, Zeyang Zhang, Ziwei Zhang, Fang Shen, Jialong Wang, Yang Li, Wenwu Zhu
Venue: KDD
Year: 2025

Graph neural architecture search (NAS) has emerged as a promising approach for autonomously designing graph neural network architectures by leveraging correlations between graphs and architectures. However, existing methods merely rely on correlations, which may be spurious and vary across distributions. This reliance, without considering causal graph-architecture relationships, limits their ability to generalize under distribution shifts that are ubiquitous in real-world graph scenarios. In this paper, we propose to handle the distribution shifts in NAS process by exploiting the causal graph-architecture relationship to search for optimal architectures that can generalize under distribution shifts. Key challenges remain unexplored: discovering causal graph-architecture relationships with stable cross-distribution predictive abilities, and leveraging them to handle distribution shifts. To address these challenges, we propose a novel approach, Causal-aware Graph Neural Architecture Search (CARNAS), which is capable of capturing causal graph-architecture relationship during NAS process and discovering optimal graph architecture under distribution shifts. We propose Disentangled Causal Subgraph Identification to extract causal subgraphs with stable predictive power across distributions, followed by Graph Embedding Intervention to intervene on these subgraphs in latent space by preserving essential features while filtering out non-causal elements, and Invariant Architecture Customization to enhance their causal invariance for optimizing graph architectures. Extensive experiments on synthetic and real-world datasets show that CARNAS enhances out-of-distribution generalization by uncovering causal graph-architecture relationships during NAS.
Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
Authors: Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, Yue-Jiao Gong
Venue: KDD
Year: 2025

Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structured policy. Concretely, on the one hand, a Relativization Filter (RF) is designed to enhance the robustness of the encoder to affine transformations of the instances, so as to potentially improve the quality of the found solutions. On the other hand, a Multi-Attentive Adaptive Active Search (MA3S) is tailored to allow the decoders to strike a balance between the optimality and diversity. Experimental evaluations on benchmark instances demonstrate the superiority of our method over recent neural baselines across different metrics, and its competitive performance against state-of-the-art traditional heuristics with significantly reduced computational time, ranging from 1.3× to 15× faster. Furthermore, we demonstrate that our method can also be applied to the Capacitated Vehicle Routing Problem (CVRP).
Unveiling Mode Connectivity in Graph Neural Network
Authors: Bingheng Li, Zhikai Chen, Haoyu Han, Shenglai Zeng, Jingzhe Liu, Jiliang Tang
Venue: KDD
Year: 2025

A fundamental challenge in understanding graph neural networks (GNNs) lies in characterizing their optimization dynamics and loss landscape geometry, critical for improving interpretability and robustness. While mode connectivity-a lens for analyzing geometric properties of loss landscapes-has proven insightful for other deep learning architectures, its implications for GNNs remain unexplored. This work presents the first investigation of mode connectivity in GNNs. We uncover that GNNs exhibit distinct non-linear mode connectivity, diverging from patterns observed in fully-connected networks or CNNs. Crucially, we demonstrate that graph structure, rather than model architecture, dominates this behavior, with graph properties like homophily correlating with mode connectivity patterns. We further establish a link between mode connectivity and generalization, proposing a generalization bound based on loss barriers and revealing its utility as a diagnostic tool. Our findings further bridge theoretical insights with practical implications: they rationalize domain alignment strategies in graph learning and provide a foundation for refining GNN training paradigms.
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems
Authors: Lichi Li, Zainul Abi Din, Zhen Tan, Sam London, Tianlong Chen, Ajay H. Daptardar
Venue: KDD
Year: 2025

In the evolving e-commerce field, recommendation systems crucially shape user experience and engagement. The rise of Consumer-to-Consumer (C2C) recommendation systems, noted for their flexibility and ease of access for customer vendors, marks a significant trend. However, the academic focus remains largely on Business-to-Consumer (B2C) models, leaving a gap filled by the limited C2C recommendation datasets that lack in item attributes, user diversity, and scale. The intricacy of C2C recommendation systems is further accentuated by the dual roles users assume as both sellers and buyers, introducing a spectrum of less uniform and varied inputs. Addressing this, we introduce MerRec, the first large-scale dataset specifically for C2C recommendations, sourced from the Mercari e-commerce platform, covering millions of users and products over 6 months in 2023. MerRec not only includes standard features such as user_id, item_id, and session_id, but also unique elements like timestamped action types, product taxonomy, and textual product attributes, offering a comprehensive dataset for research. This dataset, extensively evaluated across three recommendation tasks, establishes a new benchmark for the development of advanced recommendation algorithms in real-world scenarios, bridging the gap between academia and industry and propelling the study of C2C recommendations. Experiment code (https://github.com/mercari/mercari-ml-merrec-pub-us) and dataset (https://huggingface.co/datasets/mercari-us/merrec) are released.
Detecting Interference using Dyadic Data in Online Controlled Experiments
Authors: Yilin Li, Lu Deng, Yong Wang
Venue: KDD
Year: 2025

Interference in online controlled experiments poses significant challenges, as treatment effects may propagate through user interactions. This phenomenon systematically biases conventional treatment effect estimators and substantially complicates the interpretation of experimental results. Detecting the presence of interference is crucial for ensuring the reliability of conclusions, ultimately enhancing decision-making processes. We formalize a framework for detecting the existence of interference with dyadic data, which is ubiquitous in social network platforms. Dyadic data, typically generated from user interactions, have not been fully utilized in online controlled experiments. We first introduce the dyadic-robust inference techniques, then illustrate how the dyadic outcomes link to the user-level metrics and how interference manifests. We then propose a comprehensive approach to detect interference effects based on dyadic-robust t-statistics, and we also provide a subsampling approach to reduce the computational complexity of the proposed methods. We also provide several approaches to leverage pre-experiment data, such as selecting the optimal treatment probability, designing stratified randomization in the pre-experiment stage, or reducing the variance with both user-level and dyad-level control variates at the post-experiment stage. Our approach is computationally efficient and easily integrated with existing pipelines. We provide theoretical guarantees for our inference procedure across various asymptotic regimes. Finally, we conduct several empirical evaluations under various dyadic data-generating processes to assess the performance and robustness of the proposed procedures. Finally, we introduce an interference detection pipeline and demonstrate it with an experiment from Weixin.
Refining Labeling Functions with Limited Labeled Data
Authors: Chenjie Li, Amir Gilad, Boris Glavic, Zhengjie Miao, Sudeepa Roy
Venue: KDD
Year: 2025

Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints. However, the quality of the generated labels depends directly on the accuracy of the LFs. In this work, we study the problem of fixing LFs based on a small set of labeled examples. Towards this goal, we develop novel techniques for repairing a set of LFs by minimally changing their results on the labeled examples such that the fixed LFs ensure that (i) there is sufficient evidence for the correct label of each labeled datapoint and (ii) the accuracy of each repaired LF is sufficiently high. We model LFs as conditional rules, which enables us to refine them, i.e., to selectively change their output for some inputs. We demonstrate experimentally that our system improves the quality of LFs based on surprisingly small sets of labeled datapoints.
MGS3: A Multi-Granularity Self-Supervised Code Search Framework
Authors: Rui Li, Junfeng Kang, Qi Liu, Liyang He, Zheng Zhang, Yunhao Sha, Linbo Zhu, Zhenya Huang
Venue: KDD
Year: 2025

In the pursuit of enhancing software reusability and developer productivity, code search has emerged as a key area, aimed at retrieving code snippets relevant to functionalities based on natural language queries. Despite significant progress in self-supervised code pre-training utilizing the vast amount of code data in repositories, existing methods have primarily focused on leveraging contrastive learning to align natural language with function-level code snippets. These studies have overlooked the abundance of fine-grained (such as block-level and statement-level) code snippets prevalent within the function-level code snippets, which results in suboptimal performance across all levels of granularity. To address this problem, we first construct a multi-granularity code search dataset called MGCodeSearchNet, which contains 536K+ pairs of natural language and code snippets. Subsequently, we introduce a novel Multi-Granularity Self-Supervised contrastive learning code Search framework (MGS3). First, MGS3 features a Hierarchical Multi-Granularity Representation module (HMGR), which leverages syntactic structural relationships for hierarchical representation and aggregates fine-grained information into coarser-grained representations. Then, during the contrastive learning phase, we endeavor to construct positive samples of the same granularity for fine-grained code, and introduce in-function negative samples for fine-grained code. Finally, we conduct extensive experiments on code search benchmarks across various granularities, demonstrating that the framework exhibits outstanding performance in code search tasks of multiple granularities. These experiments also showcase its model-agnostic nature and compatibility with existing pre-trained code representation models.
Tackling Size Generalization of Graph Neural Networks on Biological Data from a Spectral Perspective
Authors: Gaotang Li, Danai Koutra, Yujun Yan
Venue: KDD
Year: 2025

We address the key challenge of size-induced distribution shifts in graph neural networks (GNNs) and their impact on the generalization of GNNs to larger graphs. Existing literature operates under diverse assumptions about distribution shifts, resulting in varying conclusions about the generalizability of GNNs. In contrast to prior work, we adopt a data-driven approach to identify and characterize the types of size-induced distribution shifts and explore their impact on GNN performance from a spectral standpoint, a perspective that has been largely underexplored. Leveraging the significant variance in graph sizes in real biological datasets, we analyze biological graphs and find that spectral differences-driven by subgraph patterns (e.g., average cycle length)-strongly correlate with GNN performance on larger, unseen graphs. Based on these insights, we propose three model-agnostic strategies to enhance GNNs' awareness of critical subgraph patterns, identifying size-intensive attention as the most effective approach. Extensive experiments with six GNN architectures and seven model-agnostic strategies across five datasets show that our size-intensive attention strategy significantly improves graph classification on test graphs 2 to 10 times larger than the training graphs, boosting F1 scores by up to 8% over strong baselines.