OpenRaiser/Intern-Atlas huggingface dataset:
Fairness-aware Prompt Tuning for Graph Neural Networks
Authors: Zhengpin Li, Minhua Lin, Jian Wang, Suhang Wang
Venue: WWW
Year: 2025

Graph prompt tuning has achieved significant success for its ability to effectively adapt pre-trained graph neural networks to various downstream tasks. However, the pre-trained models may learn discriminatory representation due to the inherent prejudice in graph-structured data. Existing graph prompt tuning overlooks such unfairness, leading to biased outputs towards certain demographic groups determined by sensitive attributes such as gender, age, and political ideology. To overcome this limitation, we propose a fairness-aware graph prompt tuning method to promote fairness while enhancing the generality of any pre-trained GNNs (named FPrompt). FPrompt introduces hybrid graph prompts to augment counterfactual data while aligning the pre-training and downstream tasks. It also applies edge modification to increase sensitivity heterophily. We provide a two-fold theoretical analysis: first, we demonstrate that FPrompt possesses universal capabilities in handling pre-trained GNN models across various pre-training strategies, ensuring its adaptability in different scenarios. Second, we show that FPrompt effectively reduces the upper bound of generalized statistical parity, thereby mitigating the bias of pre-trained models. Extensive experiments demonstrate that FPrompt outperforms baseline models in both accuracy and fairness (33%) on benchmark datasets. Additionally, we introduce a new benchmark for transferable evaluation, showing that FPrompt achieves state-of-the-art generalization performance.
GAS: Generative Auto-bidding with Post-training Search
Authors: Yewen Li, Shuai Mao, Jingtong Gao, Nan Jiang, Yunjian Xu, Qingpeng Cai, Fei Pan, Peng Jiang, Bo An
Venue: WWW
Year: 2025

Auto-bidding is essential in facilitating online advertising by automatically placing bids on behalf of advertisers. Generative auto-bidding, which generates bids based on an adjustable condition using models like transformers and diffusers, has recently emerged as a new trend due to its potential to learn optimal strategies directly from data and adjust flexibly to preferences. However, generative models suffer from low-quality data leading to a mismatch between the condition, like return to go, and true action value, especially in long sequential decision-making. Besides, the majority preference in the dataset may hinder models' generalization ability on minority advertisers' preferences. While it is possible to collect high-quality data and retrain multiple models for different preferences, the high cost makes it unaffordable, hindering the advancement of auto-bidding into the era of large foundation models. To address this, we propose a flexible and practical Generative Auto-bidding scheme using post-training Search, termed GAS, to refine a base policy model's output and adapt to various preferences. We use weak-to-strong search alignment by training small critics for different preferences and an MCTS-inspired search to refine the model's output. Specifically, a novel voting mechanism with transformer-based critics trained with policy indications could enhance search alignment performance. Additionally, utilizing the search, we provide a fine-tuning method for high-frequency preference scenarios considering computational efficiency. Extensive experiments conducted on the real-world dataset and online A/B test on the Kuaishou advertising platform demonstrate the effectiveness of GAS, achieving significant improvements, e.g., 4.60% increment of target cost.
Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
Authors: Hao Li, Yubing Ren, Yanan Cao, Yingjie Li, Fang Fang, Zheng Lin, Shi Wang
Venue: WWW
Year: 2025

Large language models (LLMs) achieve superior performance in generative tasks. However, due to the natural gap between language model generation and structured information extraction in three dimensions: task type, output format, and modeling granularity, they often fall short in structured information extraction, a crucial capability for effective data utilization on the web. In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. Furthermore, we propose the Structure2Text decider to help the language model understand the fine-grained extraction information, which converts the structured output into natural language and makes state decisions, thereby focusing on the task-specific information kernels, and alleviating language model hallucinations and incorrect content generation. We conduct extensive experiments and detailed analyses on myriad information extraction tasks, including named entity recognition, relation extraction, and event argument extraction. Our method not only achieves significant performance improvements but also considerably enhances the model's capability to generate precise and relevant content, making the extracted content easy to parse.
NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning
Authors: Ken Li, Bin Shi, Jiazhe Wei, Bo Dong
Venue: WWW
Year: 2025

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What's more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client's local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom defenses. Code is available at: https://github.com/kiyotakali/NI-GDBA
PhishIntel: Toward Practical Deployment of Reference-Based Phishing Detection
Authors: Yuexin Li, Hiok Kuek Tan, Qiaoran Meng, Mei Lin Lock, Tri Cao, Shumin Deng, Nay Oo, Hoon Wei Lim, Bryan Hooi
Venue: WWW
Year: 2025

Phishing is a critical cyber threat, exploiting deceptive tactics to compromise victims and cause significant financial losses. While reference-based phishing detectors (RBPDs) have achieved notable advancements in detection accuracy, their real-world deployment is hindered by challenges such as high latency and inefficiency in URL analysis. To address these limitations, we present PhishIntel, an end-to-end phishing detection system for real-world deployment. PhishIntel intelligently determines whether a URL can be processed immediately or not, segmenting the detection process into two distinct tasks: a fast task that checks against local blacklists and result cache, and a slow task that conducts online blacklist verification, URL crawling, and webpage analysis using an RBPD. This fast-slow task system architecture ensures low response latency while retaining the robust detection capabilities of RBPDs for zero-day phishing threats. Furthermore, we develop two downstream applications based on PhishIntel: a phishing intelligence platform and a phishing email detection plugin for Microsoft Outlook, demonstrating its practical efficacy and utility.
Efficient Epidemic Intervention Generation: A Graph Adversarial Attack Perspective
Authors: Yunxiao Li, Mingyang Wei, Zewen Liu, Max S. Y. Lau, Wei Jin
Venue: WWW
Year: 2025

Epidemic outbreaks spread through networked interactions, making graph-based modeling essential for effective intervention strategies. In this study, we propose EpiAdv, a scalable approach that integrates a modified Susceptible-Infected-Recovered (SIR) model with graph adversarial attack techniques to control infection spread on time-varying graphs. EpiAdv dynamically adjusts node states and edge weights using gradient descent, optimizing an objective function to balance infection reduction and intervention. Experimental results show that EpiAdv significantly outperforms baseline methods, such as expert-based and degree-based heuristics, in reducing infection rates while minimizing isolation costs. Furthermore, adaptability tests demonstrate the robustness of EpiAdv across different intervention timing scenarios, providing an effective and efficient framework for managing epidemics in dynamic networks.
EBaReT: Expert-guided Bag Reward Transformer for Auto Bidding
Authors: Kaiyuan Li, Pengyu Wang, Yunshan Peng, Pengjia Yuan, Yanxiang Zeng, Rui Xiang, Yanhua Cheng, Xialong Liu, Peng Jiang
Venue: WWW
Year: 2025

Reinforcement learning has been widely applied in automated bidding. Traditional approaches model bidding as a Markov Decision Process (MDP). Recently, some studies have explored using generative reinforcement learning methods to address long-term dependency issues in bidding environments. Although effective, these methods typically rely on supervised learning approaches, which are vulnerable to low data quality due to the amount of sub-optimal bids and low probability rewards resulting from the low click and conversion rates. Unfortunately, few studies have addressed these challenges. In this paper, we formalize the automated bidding as a sequence decision-making problem and propose a novel Expert-guided Bag Reward Transformer (EBaReT) to address concerns related to data quality and uncertainty rewards. Specifically, to tackle data quality issues, we generate a set of expert trajectories to serve as supplementary data in the training process and employ a Positive-Unlabeled (PU) learning-based discriminator to identify expert transitions. To ensure the decision also meets the expert level, we further design a novel expert-guided inference strategy. Moreover, to mitigate the uncertainty of rewards, we consider the transitions within a certain period as a ''bag'' and carefully design a reward function that leads to a smoother acquisition of rewards. Extensive experiments demonstrate that our model achieves superior performance compared to state-of-the-art bidding methods.
DynaProtect: A Dynamic Factor Influence Learning Framework for Protective Factor-aware Suicide Risk Prediction
Authors: Jun Li, Xiangmeng Wang, Yifei Yan, Haoyang Li, Hong Va Leong, Nancy Xiaonan Yu, Qing Li
Venue: WWW
Year: 2025

Despite significant advances in approaches to suicide detection on social media, predicting users' suicide risk in a subsequent state remains challenging. Even though existing works have identified various risk factors to improve detection performance, they often overlook the critical role of protective factors in suicide prevention. To address this limitation, we propose an approach that jointly learns both risk and protective factors to predict users' subsequent suicide risk. Recognizing that the effectiveness of these factors varies across different user patterns, we introduce a dynamic factor influence learning mechanism that captures user-dependent interactions with risk and protective factors. Our experiments demonstrate that the integrated approach significantly enhances suicide risk prediction performance compared to existing methods.
HySAE: An Efficient Semantic-Enhanced Representation Learning Model for Knowledge Hypergraph Link Prediction
Authors: Zhao Li, Xin Wang, Jun Zhao, Feng Feng, Zirui Chen, Jianxin Li
Venue: WWW
Year: 2025

Representation learning technique is an effective link prediction paradigm to alleviate the incompleteness of knowledge hypergraphs. However, the n-ary complex semantic information inherent in knowledge hypergraphs causes existing methods to face the dual limitations of weak effectiveness and low efficiency. In this paper, we propose a novel knowledge hypergraph representation learning model, HySAE, which can achieve a satisfactory trade-off between effectiveness and efficiency. Concretely, HySAE builds an efficient semantic-enhanced 3D scalable end-to-end embedding architecture to sufficiently capture knowledge hypergraph n-ary complex semantic information with fewer parameters, which can significantly reduce the computational cost of the model. In particular, we also design an efficient position-aware entity role semantic embedding way and two enhanced semantic learning strategies to further improve the effectiveness and scalability of our proposed method. Extensive experimental results on all datasets demonstrate that HySAE consistently outperforms state-of-the-art baselines, with an average improvement of 9.15%, a maximum improvement of 39.44%, an average 10.39x faster, and 75.79% fewer parameters.
CuSMer: Multimodal Intent Recognition in Customer Service via Data Augment and LLM Merge
Authors: Zhipeng Li, Binglin Wu, Yingyi Zhang, Xianneng Li, Kai Li, Weizhi Chen
Venue: WWW
Year: 2025

The increasing complexity of e-commerce customer service (CS) scenarios, driven by rapid product evolution and user base growth, presents unique challenges for intent recognition. Unlike generic user-generated content (UGC), CS-UGC exhibits multimodal complexity (e.g., product inquiries, return requests) that traditional methods struggle to address due to (1) Limited CS domain-specific knowledge, which hampers the ability of large language models (LLMs) to handle multimodal CS data, and (2) The complexity and noise in CS UGC, which undermines the robustness of traditional approaches. In this paper, we propose Customer Service Augmented LLM Merge (CuSMer), a novel framework integrating semi-supervised learning with model merging techniques through dual pipelines: (i) pseudo-labeling → fine-tuning → LLM merging and (ii) image augmentation → fine-tuning → LLM merging. These piplines enhance the robustness of LLMs against noisy, out-of-distribution data while improving their multimodal understanding of CS scenarios. Evaluated on Alibaba's real-world datasets, CuSMer demonstrates superior robustness in noisy environments and enhanced multimodal understanding compared to baseline LLMs. It achieved third place in the first round and first place in the final round in the WWW25 - Competition: Multimodal Dialogue System Intent Recognition Challenge, validating its scalability and effectiveness for industrial CS applications.
Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations
Authors: Jin Li, Shoujin Wang, Qi Zhang, Shui Yu, Fang Chen
Venue: WWW
Year: 2025

Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recently, a few efforts have sought to improve the recommendation accuracy by exploring generic structures from incomplete data. However, two significant gaps persist: 1) the difficulty in accurately generating missing data due to the limited ability to capture modality distributions; and 2) the critical but overlooked visibility bias, where items with missing modalities are more likely to be disregarded due to the prioritization of items' multimodal data over user preference alignment. This bias raises serious concerns about the fair treatment of items. To bridge these two gaps, we propose a novel Modality-Diffused Counterfactual (MoDiCF) framework for incomplete multimodal recommendations. MoDiCF features two key modules: a novel modality-diffused data completion module and a new counterfactual multimodal recommendation module. The former, equipped with a particularly designed multimodal generative framework, accurately generates and iteratively refines missing data from learned modality-specific distribution spaces. The latter, grounded in the causal perspective, effectively mitigates the negative causal effects of visibility bias and thus assures fairness in recommendations. Both modules work collaboratively to address the two aforementioned significant gaps for generating more accurate and fair results. Extensive experiments on three real-world datasets demonstrate the superior performance of MoDiCF in terms of both recommendation accuracy and fairness. The code and processed datasets are released at https://github.com/JinLi-i/MoDiCF.
SurvFormer: A Transformer Based Framework for Survival Analysis in Insurance Underwriting
Authors: Yingxue Li, Xiaolu Zhang, Jun Hu, Wenwen Xia, Ziyi Liu, Xiaobo Qin, Binjie Fei, Jun Zhou
Venue: WWW
Year: 2025

Insurance claim prediction is a critical task in the underwriting process of health insurance. It helps keep insurance programs financially stable and ensures that people have fair access to coverage. In industrial insurance systems, predictive modeling based on a large amount of user data accumulated from online insurance platforms can significantly improve underwriting accuracy. However, large amounts of policies are newly underwritten especially for some newly developed insurance products. The time lag between a policy being underwritten and a corresponding insurance claim can range from several months to years. Thus, newly acquired data may lack accurate label information when used by the traditional classification algorithm, presenting a data censoring challenge in the prediction of insurance claims. Moreover, the timing of insurance claims can be significantly influenced by distinct features associated with the policyholder's behavior and circumstances. In addressing this problem, we incorporate survival analysis into our modeling framework. We propose SurvFormer, an innovative transformer-decoder-based approach designed to estimate temporal survival probabilities. SurvFormer utilizes attention mechanisms to highlight relevant user behaviors and effectively captures inter-dependencies among temporal survival states. Furthermore, to address the prevalent issue of event-censoring imbalance in survival data, we design a survival loss called imbalance-aware censoring loss (IACL) to prevent the model from being overwhelmed by the prevalence of samples that lack precise labels. Through comprehensive experiments on three real-world data sets, SurvFormer achieves significant performance improvement and maintains high efficiency compared to the state-of-the-art baseline methods.
Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes
Authors: Yishuo Li, Qi Zhang, Wenpeng Lu, Xueping Peng, Weiyu Zhang, Jiasheng Si, Yongshun Gong, Liang Hu
Venue: WWW
Year: 2025

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients' Dynamic Treatment Regimes (DTRs) determine how patients' drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients' successive visits, which is critical to indicate patients' treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient's guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. Our code is available at: https://github.com/liyifo/MR-DTR.
Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation
Authors: Junhong Lian, Xiang Ao, Xinyu Liu, Yang Liu, Qing He
Venue: WWW
Year: 2025

Personalized news headline generation aims to provide users with attention-grabbing headlines that are tailored to their preferences. Prevailing methods focus on user-oriented content preferences, but most of them overlook the fact that diverse stylistic preferences are integral to users' panoramic interests, leading to suboptimal personalization. In view of this, we propose a novel S tylistic- C ontent A ware Pe rsonalized Headline Generation (SCAPE) framework. SCAPE extracts both content and stylistic features from headlines with the aid of large language model (LLM) collaboration. It further adaptively integrates users' long- and short-term interests through a contrastive learning-based hierarchical fusion network. By incorporating the panoramic interests into the headline generator, SCAPE reflects users' stylistic-content preferences during the generation process. Extensive experiments on the real-world dataset PENS demonstrate the superiority of SCAPE over baselines.