OpenRaiser/Intern-Atlas huggingface dataset:
Reinforcement-Learning Based Covert Social Influence Operations
Authors: Saurabh Kumar, Valerio La Gatta, Andrea Pugliese, Andrew Pulver, V. S. Subrahmanian, Jiazhi Zhang, Youzhi Zhang
Venue: WWW
Year: 2025

How might reinforcement-learning based covert social influence operations (CSIOs) be run, given that the CSIO agent wants to maximize influence and minimize discoverability of malicious accounts? And how successful can they be, given that both social platform bot detectors and humans might report them to the social platform? To answer these questions, we propose RL_CSIO, a methodology based on reinforcement learning (RL) for running CSIOs. We ran 4 CSIOs with IRB-approval over a period of 5 days using a panel of 225 human subjects. We explore 8 research questions based on the data collected. The results show that RL_CSIO agents successfully trade off influence and discoverability - but in ways that are nuanced and unexpected.
RideSmart: Pre-trained Large Models for Delivery Route Planning
Authors: Zhao Li, Yangbohan Jiao, Yuduo Shi, Donghui Ding, Jiacheng Wang, Jiarun Zhang, Haitao Xu
Venue: WWW
Year: 2025

Millions of e-bike delivery riders in China navigate complex urban environments daily, delivering a wide range of goods to hundreds of millions of customers. Their reputations-and, consequently, their earnings-are largely determined by their ability to ensure fast and timely deliveries. Despite the critical importance of efficiency, most riders rely solely on their accumulated experience to optimize their delivery routes. Notably, there is a lack of an intelligent route planning system that leverages the vast delivery knowledge embedded in the collective experience of these riders to significantly enhance delivery efficiency. In this paper, we introduce RideSmart, the first spatiotemporal pre-trained large model specifically designed for efficient delivery route optimization. Built on an extensive dataset of 1.5 million trajectory records collected from delivery riders, RideSmart utilizes pre-trained models to capture valuable rider expertise, thereby planning highly optimized routes that enable riders to complete tasks with maximum efficiency, leading to higher earnings. Experimental results show that, given the same input of origins and destinations, RideSmart consistently generates routes that are significantly more time-efficient and distance-conserving compared to those planned by riders based on their individual experience. The system achieves a reachability rate of 90.5%, with 82.4% of its generated routes being as optimal as or more efficient than human-experienced routes. By effectively utilizing the collective experience of millions of riders, RideSmart delivers substantial benefits to every individual rider. A video demonstration of RideSmart is available here: https://youtu.be/JD0N2kQTGcc.
Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
Authors: Wei Fan, Jingru Fei, Dingyu Guo, Kun Yi, Xiaozhuang Song, Haolong Xiang, Hangting Ye, Min Li
Venue: WWW
Year: 2025

Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Traditional methods towards medical time series classification rely on handcrafted feature extraction and statistical methods; with the recent advancement of artificial intelligence, the machine learning and deep learning methods have become more popular. However, existing methods often fail to fully model the complex spatial dynamics under different scales, which ignore the dynamic multi-resolution spatial and temporal joint inter-dependencies. Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. Then, to address the baseline wander problem, we propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling. Moreover, to learn the multi-view characteristics, we utilize the Frequency Convolution Networks to capture complementary information of medical time series from the frequency domain. In addition, we introduce the Multi-resolution Graph Transformer architecture to model the dynamic dependencies and fuse the information from different resolutions. Finally, we have conducted extensive experiments on multiple medical real-world datasets that demonstrate the superior performance of our method. Our Code is available at this repository: https://github.com/aikunyi/MedGNN.
Sketching Very Large-scale Dynamic Attributed Networks More Practically
Authors: Wei Wu, Shiqi Li, Ling Chen, Fangfang Li, Chuan Luo
Venue: WWW
Year: 2025

Real-world networks, particularly those in web and social media, are dynamic with evolving node attributes and structures, often involving billions of nodes and edges. Dynamic attributed network embedding is a powerful tool for capturing these changes, enabling data owners and problem owners to better understand interactions and trends for more effective engagement and decision-making. While some existing algorithms are capable of handling very large-scale dynamic attributed networks with billions of nodes and edges, they often suffer from accuracy loss or high computational overhead. In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. By the sparse random projection technique in an incremental update manner, VLS2ketch significantly reduces the energy-intensive computational workload while maintaining accuracy. Also, we introduce an information decay mechanism, which adapts to temporally varying topologies and node attributes. This mechanism ensures that outdated information gradually diminishes over time. Extensive experiments on real-world very large-scale datasets demonstrate that our proposed VLS2ketch method delivers comparable embedding quality against the state-of-the-art learning-based competitors with dramatically reduced runtime. We have released the source code and the datasets in https://github.com/AIandBD/graph-hashing/tree/main/VLS2ketch. .
STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
Authors: Wenbin Li, Di Yao, Ruibo Zhao, Wenjie Chen, Zijie Xu, Chengxue Luo, Chang Gong, Quanliang Jing, Haining Tan, Jingping Bi
Venue: WWW
Year: 2025

The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining. However, current works for evaluating the spatio-temporal understanding capability of LLMs are somewhat limited and biased. These works either fail to incorporate the latest language models or only focus on assessing a specific dimension of spatio-temporal capabilities, making the evaluation not comprehensive. To address this gap, this paper dissects LLMs' capability of spatio-temporal data into four distinct dimensions: knowledge comprehension, spatio-temporal reasoning, accurate computation, and downstream applications. We curate several natural language question-answer tasks for each category and build the benchmark dataset, namely STBench, containing 15 distinct tasks and over 70,000 QA pairs. Moreover, we have assessed the capabilities of 13 LLMs and experimental results reveal that existing LLMs show remarkable performance on knowledge comprehension and spatio-temporal reasoning tasks, with potential for further enhancement on other tasks through in-context learning, chain-of-thought prompting, and fine-tuning. The code and datasets of STBench are released on https://github.com/LwbXc/STBench.
Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods
Authors: Meng Yan, Cai Xu, Xujing Wang, Ziyu Guan, Wei Zhao, Yuhang Zhou
Venue: WWW
Year: 2025

Recommender systems based on graph neural networks perform well in tasks such as rating and ranking. However, in real-world recommendation scenarios, noise such as user misuse and malicious advertisement gradually accumulates through the message propagation mechanism. Even if existing studies mitigate their effects by reducing the noise propagation weights, the severe sparsity of the recommender system still leads to the low-weighted noisy neighbors being mistaken as meaningful information, and the prediction result obtained based on the polluted nodes is not entirely trustworthy. Therefore, it is crucial to measure the confidence of the prediction results in this highly noisy framework. Furthermore, our evaluation of the existing representative GNN-based recommendation shows that it suffers from overconfidence. Based on the above considerations, we propose a new method to quantify and calibrate the prediction confidence of GNN-based recommendations (Conf-GNNRec). Specifically, we propose a rating calibration method that dynamically adjusts excessive ratings to mitigate overconfidence based on user personalization. We also design a confidence loss function to reduce the overconfidence of negative samples and effectively improve recommendation performance. Experiments on public datasets demonstrate the validity of Conf-GNNRec in prediction confidence and recommendation performance.
Towards an Inclusive Mobile Web: A Dataset and Framework for Focusability in UI Accessibility
Authors: Ming Gu, Lei Pei, Sheng Zhou, Ming Shen, Yuxuan Wu, Zirui Gao, Ziwei Wang, Shuo Shan, Wei Jiang, Yong Li, Jiajun Bu
Venue: WWW
Year: 2025

The rapid growth of mobile web technologies has revolutionized how people manage daily activities, emphasizing the critical need for accessible mobile user interfaces (UIs) that accommodate users with disabilities and situational impairments. Current AI-driven UI understanding methods show promise but primarily target general UI modeling, neglecting nuanced, user-centric accessibility requirements. To bridge this gap, we first conducted a formative study with 12 visually impaired participants. Our study uncovers selective-accessible issues, a new class of accessibility challenges requiring finer granularity and selective focus on UI components, which existing methods largely overlook. Our findings also reveal that the severity of issues varies across interaction stages, with earlier stages posing a more significant impact. Building on these insights, we propose a comprehensive framework of three accessibility stages: focusability, information, and functionality (FIF), encompassing 12 sub-tasks under 3 overarching tasks. Identifying UI element focusability prediction (UFP) as a pivotal yet underexplored task within FIF, hindered by the absence of dedicated datasets, we introduce a new dataset (NOS) with 117,480 annotated components addressing accessibility issues comprehensively. To further enhance UFP, we introduce Graph-based UI Focusability Prediction (GIFT), a method leveraging graph neural networks to model UFP-targeted UI relationships. User studies validate the dataset's quality, while experiments show GIFT's effectiveness in improving UFP outcomes. Our code and datasets are publicly available to support further web inclusivity advancements at https://github.com/eaglelab-zju/NOS.
Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation
Authors: Zhen Zhang, Bingsheng He
Venue: WWW
Year: 2025

Unsupervised graph domain adaptation (UGDA) focuses on transferring knowledge from labeled source graph to unlabeled target graph under domain discrepancies. Most existing UGDA methods are designed to adapt information from a single source domain, which cannot effectively exploit the complementary knowledge from multiple source domains. Furthermore, their assumptions that the labeled source graphs are accessible throughout the training procedure might not be practical due to privacy, regulation, and storage concerns. In this paper, we investigate multi-source-free unsupervised graph domain adaptation, i.e., adapting knowledge from multiple source domains to an unlabeled target domain without utilizing labeled source graphs but relying solely on source pre-trained models. Unlike previous multi-source domain adaptation approaches that aggregate predictions at model level, we introduce a novel model named GraphATA which conducts adaptation at node granularity. Specifically, we parameterize each node with its own graph convolutional matrix by automatically aggregating weight matrices from multiple source models according to its local context, thus realizing dynamic adaptation over graph structured data. We also demonstrate the capability of GraphATA to generalize to both model-centric and layer-centric methods. Comprehensive experiments on various public datasets show that our GraphATA can consistently surpass recent state-of-the-art baselines with different gains.
Learning against Non-credible Second-Price Auctions
Authors: Qian Wang, Xuanzhi Xia, Zongjun Yang, Xiaotie Deng, Yuqing Kong, Zhilin Zhang, Liang Wang, Chuan Yu, Jian Xu, Bo Zheng
Venue: WWW
Year: 2025

The standard framework of online bidding algorithm design assumes that the seller commits himself to faithfully implementing the rules of the adopted auction. However, the seller may attempt to cheat in execution to increase his revenue if the auction belongs to the class of non-credible auctions. For example, in a second-price auction, the seller could create a fake bid between the highest bid and the second highest bid. This paper focuses on one such case of online bidding in repeated second-price auctions. At each time t, the winner with bid bt is charged not the highest competing bid dt but a manipulated price pt = α0 dt + (1-α0) bt, where the parameter α0 ∈ [0, 1] in essence measures the seller's credibility. Unlike classic repeated-auction settings where the bidder has access to samples (ds)s=1t-1, she can only receive mixed signals of (bs)s=1 t-1, (ds)s=1 t-1 and α0 in this problem. The task for the bidder is to learn not only the bid distributions of her competitors but also the seller's credibility. We establish regret lower bounds in various information models and provide corresponding online bidding algorithms that can achieve near-optimal performance. Specifically, we consider three cases of prior information based on whether the credibility α0 and the distribution of the highest competing bids are known. Our goal is to characterize the landscape of online bidding in non-credible second-price auctions and understand the impact of the seller's credibility on online bidding algorithm design under different information structures.
ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search
Authors: Shuai Wang, Shengyao Zhuang, Bevan Koopman, Guido Zuccon
Venue: WWW
Year: 2025

We investigate the use of Large Language Models (LLMs) for resource selection in federated search, introducing ReSLLM, a novel prompting approach. Our ReSLLM method exploits LLMs to drive the selection of resources in federated search. The ResLLM prompt uses solely information about each resource (e.g. their name) to decide on the suitability of a resource. ReSLLM can be applied in a zero-shot setting. In addition we devise an unsupervised fine tuning protocol, the Synthetic Label Augmentation Tuning (SLAT), where the relevance of previously logged queries and snippets from resources is predicted using an off-the-shelf LLM and then in turn used to fine-tune ReSLLM with respect to the task of resource selection. Our empirical evaluation and analysis details the factors influencing the effectiveness of LLMs in this context. The results showcase the merits of ReSLLM for resource selection: not only competitive effectiveness is achieved in the zero-shot setting, but also large gains are obtained when relying on the SLAT protocol for fine-tuning. Full results and code for reproduction are available at https://github.com/ielab/SLAT-RsLLM.
MRGRP: Empowering Courier Route Prediction in Food Delivery Service with Multi-Relational Graph
Authors: Chang Liu, Huan Yan, Hongjie Sui, Haomin Wen, Yuan Yuan, Yuyang Han, Hongsen Liao, Xuetao Ding, Jinghua Hao, Yong Li
Venue: WWW
Year: 2025

Instant food delivery has become one of the most popular web services worldwide due to its convenience in our daily lives. A fundamental problem in this service scenario is the accurate prediction of courier routes, which is essential for optimizing task dispatch and thereby improving delivery efficiency. It not only increases the satisfaction of both couriers and users but also drives higher profitability for the platform. The deployed heuristic prediction approach of the platform accounts for only limited human-selected task features and neglects couriers' preferences, resulting in sub-optimal performances. Moreover, existing learning-based methods fail to adequately explore the diverse factors influencing couriers' decision-making behaviors and intricate relationships among factors. To meet the urgent need for a powerful route prediction approach that benefits millions of couriers, users, and the platform itself, we propose a M ulti- R elational G raph-based R oute P rediction (MRGRP) method, which enables fine-grained modeling of the correlations among tasks influencing couriers' decision-making and achieves accurate prediction. We encode spatial and temporal proximity, along with the pickup-delivery relationships of tasks, into a multi-relational graph, then design a GraphFormer architecture to capture these complex correlations. Furthermore, we introduce a route decoder that leverages information about couriers as well as dynamic distance and time contexts for dynamic prediction. It also utilizes existing route solutions as a reference to find better outcomes. Experimental results demonstrate that our proposed model attains state-of-the-art performance in route prediction on offline data from cities of diverse scales. We then deploy our model on the Meituan Turing online platform, where it significantly surpasses the existing deployed heuristic algorithm and achieves a high route prediction accuracy of 0.819, which is required for the quality and satisfaction of couriers and users in the instant food delivery service. We have open-sourced our code at https://github.com/tsinghua-fib-lab/MGRoute.
TIME 2025: 1st International Workshop on Transformative Insights in Multi-faceted Evaluation
Authors: Lei Wang, Md. Zakir Hossain, Syed M. S. Islam, Tom Gedeon, Sharifa Alghowinem, Isabella Yu, Serena Bono, Xuanying Zhu, Gennie Nguyen, Nur Al Hasan Haldar, Seyed Mohammad Jafar Jalali, Md. Abdur Razzaque, Imran Razzak, Md. Rafiqul Islam, Shahadat Uddin, Naeem Janjua, Aneesh Krishna, Manzur Ashraf
Venue: WWW
Year: 2025

Our workshop brings together domain experts and research students to share insights, practical guidance, and evaluations on key topics, including social network analysis, graph algorithms, web mining, semantics and knowledge, security, privacy, fairness, and ethics on the web. We invite survey, evaluation, or review papers that critically analyze models and datasets from diverse perspectives. These papers serve as essential resources by (i) providing quick reference guides for researchers and practitioners, (ii) enhancing accessibility for newcomers, and (iii) distilling key insights into actionable knowledge. Complementing these contributions, invited talks from experts and industry leaders will offer practical perspectives, fostering cross-domain collaboration in web technologies. Through thought-provoking discussions and networking opportunities, the workshop bridges research and real-world applications, setting a new standard for interdisciplinary exchange in the field.
TESA: A Trajectory and Semantic-aware Dynamic Heterogeneous Graph Neural Network
Authors: Xin Wang, Jiawei Jiang, Xiao Yan, Qiang Huang
Venue: WWW
Year: 2025

Dynamic graph neural networks (DGNNs) are designed to capture the dynamic evolution of graph node interactions. However, existing DGNNs mainly consider homogeneous graphs, neglecting the rich heterogeneity in node and edge types, which is prevalent for real-world graphs and essential for modeling complex dynamic interactions. In this work, we propose the TrajEctory and Semantic-Aware dynamic heterogeneous graph neural network (TeSa), which integrates trajectory-based evolution and semantic-aware aggregation to capture both the evolving dynamics and heterogeneous semantics entailed in continuous-time dynamic heterogeneous graphs. In particular, trajectory-based evolution treats the interactions received by each node (called node trajectory) as a sequence and employs a temporal point process to learn the dynamic evolution in these interactions. Semantic-aware aggregation separates edges of different types when aggregating messages for each node from its neighbors. Edges of the same type are processed at first (i.e., intra-semantic aggregation), and then edges of different types are handled (i.e., inter-semantic fusion), to offer a comprehensive view of the heterogeneous semantics. We compare TeSa with 7 state-of-the-art DGNN models, and the results show that TeSa improves the best-performing baseline by an average of 5.11% and 5.74% in accuracy for transductive and inductive tasks.
A Competitive-Cooperative Approach to Dynamic Pricing for Two-Sided Hospitality Platforms
Authors: Hadi Mohammadzadeh Abachi, Majid Namazi, Milad Mosharraf, Pooyan Asgari, Amin Beheshti
Venue: WWW
Year: 2025

Fair pricing as an essential scope in the two-sided hospitality web platforms, refers to setting room rates that balance affordability for guests with profitability for the hotel, taking into account factors like market demand, seasonality, and competitive pricing. Unfair pricing practices can lead to customer dissatisfaction and disrupt the competitive balance within the industry. This study extends the FairPlay method, a cooperative game-theoretic approach designed to balance service provider and customer interests, by incorporating local competitor analysis at the room and hotel level. While FairPlay effectively addresses competitive dynamics, it lacks the consideration of regional and agglomeration effects, which are critical for capturing spatial market influences and competitive overlap among hotels in nearby areas. To address this gap, we introduce an enhanced method that utilizes graph node embedding techniques to model regional proximity and inter-dependencies between hotels. By leveraging these embeddings, our approach attempts to adapt to market dynamics that traditional models often miss. Our results demonstrate that embedding-based methodology outputs fairer and more competitive pricing, offering real-time adjustments that better align with local market demands and competitor behaviour. This enhancement significantly improves upon the FairPlay model by integrating regional effects, resulting in more equitable and balanced pricing for the hospitality industry.