OpenRaiser/Intern-Atlas huggingface dataset:
Bridging Fairness and Uncertainty: Theoretical Insights and Practical Strategies for Equalized Coverage in GNNs
Authors: Longfeng Wu, Yao Zhou, Jian Kang, Dawei Zhou
Venue: WWW
Year: 2025

Graph Neural Networks (GNNs) have become indispensable tools in many domains, such as social network analysis, financial fraud detection, and drug discovery. Prior research primarily concentrated on improving prediction accuracy while overlooking how reliable the model predictions are. Conformal prediction on graphs emerges as a promising solution, offering statistically sound uncertainty estimates with a pre-defined coverage level. Despite the promising progress, existing works only focus on achieving model coverage guarantees without considering fairness in the coverage within different demographic groups. To bridge the gap between conformal prediction and fair coverage across different groups, we pose the fundamental question: Can fair GNNs enable the uncertainty estimates to be fairly applied across demographic groups? To answer this question, we provide a comprehensive analysis of the uncertainty estimation in fair GNNs employing various strategies. We prove theoretically that fair GNNs can enforce consistent uncertainty bounds across different demographic groups, thereby minimizing bias in uncertainty estimates. Furthermore, we conduct extensive experiments on five commonly used datasets across seven state-of-the-art fair GNN models to validate our theoretical findings. Additionally, based on the theoretical and empirical insights, we identify and analyze the key strategies from various fair GNN models that contribute to ensuring equalized uncertainty estimates. Our work estimates a solid foundation for future exploration of the practical implications and potential adjustments needed to enhance fairness in GNN applications across various domains. For reproducibility, we publish our data and code at https://github.com/wulongfeng/EqualizedCoverage_CP.
Generative Prompting for Complex Product Retrieval
Authors: Nan Xi, Jingjing Meng, Yitian Chen, Chaosheng Dong, Yan Gao, Yi Sun, Junsong Yuan
Venue: WWW
Year: 2025

Complex product retrieval, which involves interpreting complicated language contexts that may not align directly with product metadata, presents significant challenges for accurate query-product matching compared to traditional keyword-based search methods. Earlier approaches that rely on pretrained language models for generating text embeddings in a shared space have shown promise but fall short in leveraging multi-modal context, particularly visual cues. Motivated by Generation-Augmented Retrieval (GAR), we propose a novel method for complex product search that enhances language-heavy queries with generative visual prompts. Our approach incorporates a pretrained LLM to map these enriched queries and product metadata into a unified embedding space, thereby improving relevance matching without necessitating product images. Experiments on the Amazon-C4 dataset demonstrate the superior performance of our method, consistently surpassing state-of-the-art techniques across various product categories. Our contributions include a generation-augmented retrieval model, an innovative use of visual prompts for query enhancement, and a comprehensive evaluation showing the effectiveness of our approach in complex retrieval tasks.
Frequency-aware Graph Signal Processing for Collaborative Filtering
Authors: Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu
Venue: WWW
Year: 2025

Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention for high efficiency. However, these methods failed to utilize user/item unique characteristics, as well as user and item high-order neighborhood information when modeling user preference, leading to sub-optimal performance. To this end, we propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering (CF). Firstly, we design a Cascaded Filter Module to capture both unique and common user/item characteristics to more accurately model user preference. Then, we devise a Parallel Filter Module to fully utilize high-order neighborhood information of users/items for more accurate user preference modeling. Finally, we combine these modules via a linear model to further improve recommendation accuracy. Extensive experiments demonstrate the superiority of our method from prediction accuracy and training efficiency.
Semantic-tailored Variational-Contrastive Graph Learning for Cognitive Diagnosis
Authors: Chenao Xia, Fei Liu, Zihan Wang, Zhuangzhuang He, Pengyang Shao, Haowei Zhou, Yonghui Yang
Venue: WWW
Year: 2025

Cognitive diagnosis (CD), the foundation of intelligent education, aims to assess students' cognitive levels in knowledge concepts. Graph-based CD enhances diagnostic performance by incorporating high-order relations among entities, such as students, exercises, and knowledge concepts. Recently, self-supervised learning has been applied to CD to address data sparsity. However, existing contrastive learning methods may distort the student-exercise graph and overlook important semantic heterogeneity between correct and incorrect response logs. To address these limitations, we propose the Semantic-tailored Variational-Contrastive Graph Cognitive Diagnosis (SVGCD) method. First, a semantic-aware GNN is used to generate entity representations for different semantic environments. Then, a semantic-specific variational graph reconstruction module infers representation distributions and reconstructs semantic subgraphs while preserving the original graph structure. Additionally, a semantic-specific contrastive strategy introduces high-quality self-supervised signals while retaining semantic characteristics, enhancing student modeling for CD. Extensive experiments on two real-world datasets validate the effectiveness of our SVGCD. The code is available at https://github.com/XChuckie/SVGCD.
Community Detection in Large-Scale Complex Networks via Structural Entropy Game
Authors: Yantuan Xian, Pu Li, Hao Peng, Zhengtao Yu, Yan Xiang, Philip S. Yu
Venue: WWW
Year: 2025

Community detection is a critical task in graph theory, social network analysis, and bioinformatics, where communities are defined as clusters of densely interconnected nodes. However, detecting communities in large-scale networks with millions of nodes and billions of edges remains challenging due to the inefficiency and unreliability of existing methods. Moreover, many current approaches are limited to specific graph types, such as unweighted or undirected graphs, reducing their broader applicability. To address these issues, we propose a novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional (2D) structural entropy within a potential game framework. In the game, nodes decide to stay in the current community or move to another based on a strategy that maximizes the 2D structural entropy utility function. Additionally, we introduce a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity. Experimental results on real-world networks demonstrate that CoDeSEG is the fastest method available and achieves state-of-the-art performance in overlapping normalized mutual information (ONMI) and F1 scores.
Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection
Authors: Sheng Xiang, Yidong Jiang, Yunting Chen, Dawei Cheng, Guoping Zhao, Changjun Jiang
Venue: WWW
Year: 2025

Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focus on isolated node features, often overlooking the broader context of interconnected nodes. Graph-based techniques, particularly Graph Neural Networks (GNNs), have advanced the field by leveraging relational information effectively. However, in real-world spoofing detection datasets, trading behaviors exhibit dynamic, irregular patterns. Existing spoofing detection methods, though effective in some scenarios, struggle to capture the complexity of dynamic and diverse, evolving inter-node relationships. To address these challenges, we propose a novel framework called the Generative Dynamic Graph Model (GDGM), which models dynamic trading behaviors and the relationships among nodes to learn representations for conspiracy spoofing detection. Specifically, our approach incorporates the generative dynamic latent space to capture the temporal patterns and evolving market conditions. Raw trading data is first converted into time-stamped sequences. Then we model trading behaviors using the neural ordinary differential equations and gated recurrent units, to generate the representation incorporating temporal dynamics of spoofing patterns. Furthermore, pseudo-label generation and heterogeneous aggregation techniques are employed to gather relevant information and enhance the detection performance for conspiratorial spoofing behaviors. Experiments conducted on spoofing detection datasets demonstrate that our approach outperforms state-of-the-art models in detection accuracy. Additionally, our spoofing detection system has been successfully deployed in one of the largest global trading markets, further validating the practical applicability and performance of the proposed method.
Dynamic Detection of Sarcasm Topic-Target Pairs via LLM-Based Knowledge Alignment
Authors: Mengyu Xiang, Yuxuan Song, Qiudan Li, Shu Wu, Daniel Dajun Zeng
Venue: WWW
Year: 2025

Dynamically capturing topic-target pairs can provide a mechanism explaining the reason that triggers the sarcasm. Existing approaches ignore the event evolution in real-world scenario. Hot topics reflecting the trends of events provide external knowledge clues such as topic tags and posts that change over time. How to eliminate the noise and conflicts existed in the knowledge at different time is a key challenge. This paper proposes a Knowledge Alignment method based on a Large Language Model (KA-LLM) for dynamic detection of topic-target pairs. Guided by knowledge clues, the LLM dynamically adjusts the topological structure of the knowledge graph, enabling the knowledge features to pay more attention to the current events. A hybrid alignment approach is designed to achieve knowledge fusion through feature contrast and reconstruction. The effectiveness of the proposed method is validated on digital and automobile datasets.
High-Performance and Secure Jaccard Similarity Estimation for Cloud Storage
Authors: Zhixiong Xie, Wenlong Tian, Jianfeng Lu, Weijun Xiao, Zhiyong Xu
Venue: WWW
Year: 2025

Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks. Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements. Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures. To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method. Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure. Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods.
Diffusion-based Graph-agnostic Clustering
Authors: Kun Xie, Renchi Yang, Sibo Wang
Venue: WWW
Year: 2025

Clustering over a graph seeks to partition the nodes therein into disjoint groups such that nodes within the same cluster are tightly-knit, while those across clusters are distant from each other. In practice, graphs are often attended with rich attributes, which are termed attributed graphs. By leveraging the complementary nature of graph topology and node attributes in such graphs, graph neural networks (GNNs) have obtained encouraging performance in graph clustering. However, existing GNN-based approaches strongly rely on the homophilic assumption of the input graph, and thus, largely fail on heterophilic graphs and others embodying numerous missing or noisy links, which are widely present in real life. To bridge this gap, this paper presents DGAC, an effective graph-agnostic solution for graph clustering. Particularly, DGAC overcomes the limitations of prior works by exploiting the high-order connectivity of nodes within not only the input graph G but also the affinity graph H underlying the attribute data. To achieve this goal, we first unify the embedding and clustering generations into a coherent framework optimizing the Dirichlet Energy on both G and H. Based thereon, theoretically-grounded solvers are developed for efficient constructions of the embeddings and clusters via graph diffusion operations, which aggregate features from specific neighbors, enabling the capture of high-order semantics from G or H. On top of that, DGAC includes three training loss functions that facilitate effective feature extraction and clustering. Extensive experiments, comparing DGAC against 12 baselines over 12 homophilic or heterophilic graph datasets, showcase that DGAC consistently and considerably outperforms all competitors in terms of clustering quality measured against ground truth labels.
ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems
Authors: Haibo Xing, Kanefumi Matsuyama, Hao Deng, Jinxin Hu, Yu Zhang, Xiaoyi Zeng
Venue: WWW
Year: 2025

Industrial recommendation systems typically involve a two-stage process: retrieval and ranking, which aims to match users with millions of items. In the retrieval stage, classic embedding-based retrieval (EBR) methods depend on effective negative sampling techniques to enhance both performance and efficiency. However, existing techniques often suffer from false negatives, high cost for ensuring sampling quality and semantic information deficiency. To address these limitations, we propose Effective and Semantic-Aware Negative Sampling (ESANS), which integrates two key components: Effective Dense Interpolation Strategy (EDIS) and Multimodal Semantic-Aware Clustering (MSAC). EDIS generates virtual samples within the low-dimensional embedding space to improve the diversity and density of the sampling distribution while minimizing computational costs. MSAC refines the negative sampling distribution by hierarchically clustering item representations based on multimodal information (visual, textual, behavioral), ensuring semantic consistency and reducing false negatives. Extensive offline and online experiments demonstrate the superior efficiency and performance of ESANS.
MaSH: Maximal Separating Poincaré Hyperplanes for Hierarchical Imbalanced Learning
Authors: Bo Xiong, Jiaying Lu, Yuqicheng Zhu, Carl Yang
Venue: WWW
Year: 2025

Hyperbolic classifiers, which typically view hyperbolic hyperplanes as decision boundaries, are generalizations of Euclidean classifiers in hyperbolic space suitable for modeling hierarchical data. In this paper, we present Maximal Separating Poincaré Hyperplane (MaSH), a hyperbolic geometric inductive bias that enhances the generalization capability of hyperbolic classifiers, especially on the class-imbalanced settings. MaSH encourages 1) the equiangularity of the ideal points of the Poincaré hyperplanes of all classes; and 2) the equiradiality of these Poincaré hyperplanes. The two properties jointly encourage the maximal separation bias for hyperbolic classifiers. We perform experiments on imbalanced/long-tailed classification and the results show consistent improvements.
Heterogeneous Graph Transfer Learning for Category-aware Cross-Domain Sequential Recommendation
Authors: Zitao Xu, Xiaoqing Chen, Weike Pan, Zhong Ming
Venue: WWW
Year: 2025

Cross-domain sequential recommendation (CDSR) is proposed to alleviate the data sparsity issue while capturing users' sequential preferences. However, most existing methods do not explore the item transition patterns across different domains and can also not be applied to a multi-domain scenario.Moreover, previous methods rely on overlapping users as bridges to transfer knowledge, which struggles to capture the complex associations across domains without sufficient overlapping users. In this paper, we introduce item attributes into CDSR, and propose a heterogeneous graph transfer learning method to address these issues.Specifically, we construct a cross-domain heterogeneous graph to allow the association of user, item, and category nodes from different domains,and enhance the flexibility of the model by enabling message propagation between more nodes through edge expansion based on the semantic similarity and co-occurrence probability.In addition, we devise meta-paths from different perspectives for nodes at item, user and category levels to guide information aggregation, which can transfer knowledge across domains and reduce the reliance on the number of overlapping users.We further design attention modules to capture users' dynamic preferences from the item sequences they have interacted with in each domain, and explore the transition patterns within category sequences which reflect users' coarse-grained preferences.Finally, we perform knowledge transfer across different domains, and predict the most likely items that users will interact with in each domain. Extensive empirical studies on three real-world datasets indicate that our HGTL significantly outperforms the state-of-the-art baselines in all cases.
Personalized Image Generation with Large Multimodal Models
Authors: Yiyan Xu, Wenjie Wang, Yang Zhang, Biao Tang, Peng Yan, Fuli Feng, Xiangnan He
Venue: WWW
Year: 2025

Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing content and are constrained by its limited diversity, making it difficult to meet users' varied content needs. To address this limitation, personalized content generation has emerged as a promising direction with broad applications. Nevertheless, most existing research focuses on personalized text generation, with relatively little attention given to personalized image generation. The limited work in personalized image generation faces challenges in accurately capturing users' visual preferences and needs from noisy user-interacted images and complex multimodal instructions. Worse still, there is a lack of supervised data for training personalized image generation models. To overcome the challenges, we propose a Personalized Image Generation Framework named Pigeon, which adopts exceptional large multimodal models with three dedicated modules to capture users' visual preferences and needs from noisy user history and multimodal instructions. To alleviate the data scarcity, we introduce a two-stage preference alignment scheme, comprising masked preference reconstruction and pairwise preference alignment, to align Pigeon with the personalized image generation task. We apply Pigeon to personalized sticker and movie poster generation, where extensive quantitative results and human evaluation highlight its superiority over various generative baselines.
MDAM3: A Misinformation Detection and Analysis Framework for Multitype Multimodal Media
Authors: Qingzheng Xu, Heming Du, Szymon Lukasik, Tianqing Zhu, Sen Wang, Xin Yu
Venue: WWW
Year: 2025

Misinformation is a significant societal issue with potentially severe consequences. It appears in text, image, audio, and video modalities, encompassing various categories such as unimodal deception (fact-conflicting, AI-generated & offensive content) and cross-modal inconsistencies. However, current detection approaches often focus on text and image, overlooking the growing prevalence of misinformation in audio and video content. Moreover, these methods typically tend to address only one or two types of misinformation, failing to address all categories simultaneously. These detectors are also usually designed to make judgments without providing explanations, reducing transparency and limiting their broader applicability. To address these issues, we propose MDAM3, a Misinformation Detection and Analysis Framework for Multitype Multimodal Media. MDAM3 analyzes each input in internal detection and examines relationships across modalities to identify inconsistencies. It utilizes web resources and integrates Large Vision-Language Models (LVLMs) to deliver accurate detection results along with detailed analysis. To evaluate MDAM3, we curate MDAM3-DB, a specialized multitype multimodal misinformation dataset. A user study is conducted to explore MDAM3's usability, interpretability, and effectiveness. We hope this research contributes to advancing misinformation detection methodologies and provides valuable insights for developing robust multimodal analysis tools.
Fair Personalized Learner Modeling Without Sensitive Attributes
Authors: Hefei Xu, Min Hou, Le Wu, Fei Liu, Yonghui Yang, Haoyue Bai, Richang Hong, Meng Wang
Venue: WWW
Year: 2025

Personalized learner modeling uses learners' historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD). This is essential for web-based learning services such as learning resource recommendation and adaptive testing. However, prior studies have shown that CD models may unfairly correlate learners' abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes. While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical. Furthermore, the presence of multiple sensitive attributes complicates fairness improvements. In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. We introduce a novel fairness objective tailored for personalized learner modeling and design a max-min strategy to facilitate both sensitive information inference and fair CD modeling. In the max step, we infer pseudo-labels by maximizing the fairness objective, while in the min step, we retrain the CD model by minimizing it. Additionally, we provide a theoretical guarantee that our framework reduces the upper bound of fairness generalization error. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods. Our code is available at: https://github.com/HeFei-X/FairWISA.
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
Authors: Jian Xu, Sichun Luo, Xiangyu Chen, Haoming Huang, Hanxu Hou, Linqi Song
Venue: WWW
Year: 2025

Large Language Models (LLMs) have been integrated into recommendation systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further incorporated into these systems to retrieve more relevant items and improve system performance. However, existing RAG methods rely primarily on textual semantics and often fail to incorporate the most relevant items, limiting the effectiveness of the systems. In this paper, we propose Representation learning for retrieval-Augmented Large Language model Recommendation (RALLRec). Specifically, we enhance textual semantics by prompting LLMs to generate more detailed item descriptions, followed by joint representation learning of textual and collaborative semantics, which are extracted by the LLM and recommendation models, respectively. Considering the potential time-varying characteristics of user interest, a simple yet effective reranking method is further introduced to capture the dynamics of user preference. We conducted extensive experiments on three real-world datasets, and the evaluation results validated the effectiveness of our method. Code is made public at https://github.com/JianXu95/RALLRec.