OpenRaiser/Intern-Atlas huggingface dataset:
UltraFilter: A Privacy-Preserving Bloom Filter
Authors: Kenneth Odoh
Venue: WWW
Year: 2025

Recent work by Reviriego et al. [12] has shown susceptibility to privacy leaks for elements stored in the Bloom Filter due to the possibility of recovery attacks despite the indirection (unlinkability) afforded by the underlying hash functions. As a result, we introduced a data structure known as UltraFilter to allow for approximate set membership checks without the pre-existing privacy issues in the standard Bloom filter by employing differential privacy. We provide a threefold contribution: First, we achieve tunable privacy by modifying the noise, ε, to adjust the reconstruction error that trades off utility for privacy protection in UltraFilter. Second, we provide a set of proofs to validate our formulation and a working implementation1 for easy replication. Finally, we demonstrate the usefulness of our formulation in an ablation study.
WebTableX: Efficiently Discovering Web Table Transformations Through Sampling
Authors: Soroush Omidvartehrani, Arash Dargahi Nobari, Davood Rafiei
Venue: WWW
Year: 2025

Describing real-world entities varies widely across online sources, complicating the task of web data integration. We study the challenge of efficiently discovering syntactic transformations for web tables, where two tables are not initially equi-joinable but can become joinable after some transformations. Detecting these transformations is challenging due to the large space of possible candidates, which further grows with the number of rows. To overcome this, we introduce a cluster-based table sampling that reduces the number of required rows for transformation generation by capturing underlying data patterns. Our extensive evaluation demonstrates that the proposed method significantly improves the runtime performance of state-of-the-art methods on two real-world web datasets, sourced from webpage content and organizational forums, by one to two orders of magnitude with negligible impact on accuracy.
Hidden Impact of Hardware Technologies on Throughput: a Case Study on a Brazilian Mobile Web Network
Authors: Eduardo C. Paim, Roberto Irajá Tavares da Costa Filho, Valter Roesler, Theophilus A. Benson, Alberto Schaeffer-Filho
Venue: WWW
Year: 2025

The Web has shifted towards a mobile-first ecosystem with tools, frameworks, and forums explicitly discussing and catering for the mobile users, both mobile apps and mobile web-pages. Unfortunately, much of the studies and designs are often based on analysis and findings from developed regions (e.g., N. America and Europe) or based on user-generated data (introducing bias). In this paper, we present one of the first studies to understand the interplay between hardware characteristics (e.g., cellular and mobile) on expected network and application level performance in Brazil (the largest developing region in S. America). We analyze more than 170 million measurement sessions collected from within the network of one of the largest Mobile Network Operators in Brazil. Our findings (1) illustrate limitations of existing crowdsourced measurements and inaccuracies in assumptions about adoption patterns and performance in the global south, (2) highlight the differences between recommendations made by standardization bodies and real world performance, (3) disclose a significant change pre- and post-pandemic, and (4) quantify the benefits of using both client side and network data for analysis.
On the Cross-Graph Transferability of Dynamic Link Prediction
Authors: Zhiqiang Pan, Chen Gao, Fei Cai, Wanyu Chen, Xin Zhang, Honghui Chen, Yong Li
Venue: WWW
Year: 2025

Dynamic link prediction aims to predict the future links on dynamic graphs, which can be applied to wide scenarios such as recommender systems and social networks on the World Wide Web. Existing methods mainly (1) focus on the in-graph learning, which cannot generalize to graphs unobserved during training; or (2) achieve the cross-graph predictions in a many-many mechanism by training on multiple graphs across various domains, which results in a large computational cost. In this paper, we propose a cross-graph dynamic link predictor named CrossDyG, which achieves the cross-graph transferability in a one-many mechanism which trains on one single source graph and test on different target graphs. Specifically, we provide causal and empirical analysis on the structural bias caused by the graph-specific structural characteristics in cross-graph predictions. Then, we conduct deconfounded training to learn the universal network evolution pattern from one single source graph during training. Finally, we apply the causal intervention to leverage the graph-specific structural characteristics of each target graph during inference. Extensive experiments conducted on three benchmark data of dynamic graphs demonstrate that CrossDyG outperforms the state-of-the-art baselines by up to 11.01% and 17.02% in terms of AP and AUC, respectively. In addition, the improvements are especially significant when training on small source graphs.
Logic-Aware Knowledge Graph Reasoning for Structural Sparsity under Large Language Model Supervision
Authors: Yudai Pan, Jiajie Hong, Tianzhe Zhao, Lingyun Song, Jun Liu, Xuequn Shang
Venue: WWW
Year: 2025

Knowledge Graph (KG) reasoning aims to predict missing entities in incomplete triples, which requires adequate structural information to derive accurate embeddings. However, KGs in the real world are not as dense as the idealized benchmarks, where sparse graph structures restrict the comprehensive structural information for superior performance. Although the logical semantics in KGs shows its potential in alleviating the impact of structural sparsity, there still exist some challenges. The deficient supervision and the semantic gap of logic make it difficult to introduce logical semantics in sparse KG reasoning. To this end, we propose a novel KG reasoning approach LoLLM injecting logic with the supervised information supplied by the Large Language Model (LLM), which is proved to be effective in evaluating and scoring. Firstly, LoLLM derives structural embeddings employing a graph convolutional network (GCN) with relation-aware and triple-aware attention. LoLLM secondly constructs reasoning paths instantiated from the first-order logic rules extracted from sparse KGs, and injects the logical semantics by a designed LLM-enhanced tuning strategy. We propose a textual loss (TL) and a logical loss (LL) in the optimization and obtain logical tuning embeddings of KG in this process. Finally, LoLLM fuses structural embeddings from the GCN and logical tuning embeddings from the LLM-enhanced tuning for scoring and incomplete triple prediction. Extensive experiments on two sparse KGs and a benchmark show that LoLLM outperforms state-of-the-art structure-based and Language Model (LM)-augmented baselines. Moreover, the logic rules with corresponding confidences provide explicit explanations as an interpretable paradigm.
Differentially Private Bayesian Persuasion
Authors: Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng
Venue: WWW
Year: 2025

The tension between persuasion and privacy preservation is common in real-world settings. Online platforms should protect the privacy of web users whose data they collect, even as they seek to disclose information about these data (e.g., to advertisers). Similarly, hospitals may share patient data to attract research investments with the obligation to preserve patients' privacy. To address these issues, we study Bayesian persuasion under differential privacy constraints, where the sender must design an optimal signaling scheme for persuasion while guaranteeing the privacy of each agent's private information in the database. To understand how privacy constraints affect information disclosure, we explore two perspectives within Bayesian persuasion: one views the mechanism as releasing a posterior about the private data, while the other views it as sending an action recommendation. The posterior-based formulation leads to privacy-utility tradeoffs, quantifying how the tightness of privacy constraints impacts the sender's optimal utility. For any instance in a common utility function family and a wide range of privacy levels, a significant constant gap in the sender's optimal utility can be found between any two of the three conditions: ε-differential privacy constraint, relaxation (ε,δ)-differential privacy constraint, and no privacy constraint. We further geometrically characterize optimal signaling schemes under popular privacy constraints (ε-differential privacy, (ε,δ)-differential privacy and Rényi differential privacy), which turns out to be equivalent to finding concave hulls in constrained posterior regions. Finally, we develop polynomial-time algorithms for computing optimal differentially private signaling schemes.
Computational Advertising: Recent Advances
Authors: Junwei Pan, Zhilin Zhang, Han Zhu, Jian Xu, Jie Jiang, Bo Zheng
Venue: WWW
Year: 2025

Computational advertising is one of the most successful application scenarios of machine learning and artificial intelligence. This tutorial is designed to review the latest progress of several critical areas in computational advertising: matching, prediction, auction and bidding. Particularly, with the recent advances in generative AI such as large language models, there is a growing interest in further enhancing these areas with these techniques. In this tutorial, we first introduce the recent advances in matching, including its architecture alternatives, model developments, and how it co-evolves with the ad products which distinguishes itself from that in recommendation products. We then review the recent advances in prediction, with a focus on topics such as feature interactions, user interest models, and multi-task/domain learning. We will show how these building bricks constitute large prediction models and LLM-enhanced/LLM-based prediction models. Then, we discuss auction and bidding, a unique area in computational advertising. Both traditional and learning-based auctions will be introduced, followed by their applications in real-world ad products. Given the auction designs, we show how bidding evolves from control-based, to reinforcement learning-based, and most recently to generative AI-based. Our aim is to help the audience grasp the recent developments in computational advertising, as well as to spark inspiration for future research.
MAML: Towards a Faster Web in Developing Regions
Authors: Ayush Pandey, Matteo Varvello, Syed Ishtiaque Ahmed, Shurui Zhou, Lakshmi Subramanian, Yasir Zaki
Venue: WWW
Year: 2025

The web experience in developing regions remains subpar, primarily due to the growing complexity of modern webpages and insufficient optimization by content providers. Users in these regions typically rely on low-end devices and limited bandwidth, which results in a poor user experience as they download and parse webpages bloated with excessive third-party CSS and JavaScript (JS). To address these challenges, we introduce the Mobile Application Markup Language (MAML), a flat layout-based web specification language that reduces computational and data transmission demands, while replacing the excessive bloat from JS with a new scripting language centered on essential (and popular) web functionalities. Last but not least, MAML is backward compatible as it can be transpiled to minimal HTML/JavaScript/CSS and thus work with legacy browsers. We benchmark MAML in terms of page load times and sizes, using a translator which can automatically port any webpage to MAML. When compared to the popular Google AMP, across 100 testing webpages, MAML offers webpage speedups by tens of seconds under challenging network conditions thanks to its significant size reductions. Next, we run a competition involving 25 university students porting 50 of the above webpages to MAML using a web-based editor we developed. This experiment verifies that, with little developer effort, MAML is quite effective in maintaining the visual and functional correctness of the originating webpages.
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
Authors: Ming Pang, Chunyuan Yuan, Xiaoyu He, Zheng Fang, Donghao Xie, Fanyi Qu, Xue Jiang, Changping Peng, Zhangang Lin, Zheng Luo, Jingping Shao
Venue: WWW
Year: 2025

The retrieval module is a crucial component of search systems. Traditional sparse and dense retrieval methods struggle to leverage general world knowledge and often fail to capture the nuanced features of queries and products. With the advent of large language models (LLMs), industrial search systems have started to employ LLMs to generate identifiers for product retrieval. Commonly used identifiers include (1) static/semantic IDs and (2) product term sets. The first approach requires creating a product ID system from scratch, missing out on the world knowledge embedded within LLMs. While the second approach leverages this general knowledge, the significant difference in word distribution between queries and products means that product-based identifiers often do not align well with user search queries, leading to missed product recalls. Furthermore, when queries contain numerous attributes, these algorithms generate a large number of identifiers, making it difficult to assess their quality, which results in low overall recall efficiency. To address these challenges, this paper introduces a novel e-commerce retrieval paradigm: the Generative Retrieval and Alignment Model (GRAM). GRAM employs joint training on text information from both queries and products to generate shared text identifier codes, effectively bridging the gap between queries and products. This approach not only enhances the connection between queries and products but also improves inference efficiency. The model uses a co-alignment strategy to generate codes optimized for maximizing retrieval efficiency. Additionally, it introduces a query-product scoring mechanism to compare product values across different codes, further boosting retrieval efficiency. By integrating these scores, GRAM can replace the traditional recall and initial ranking stages, achieving an integrated retrieval and pre-ranking process. Extensive offline and online A/B testing was conducted. The results demonstrate that GRAM significantly outperforms traditional sparse and dense retrieval algorithms and the latest generative retrieval models, confirming its effectiveness and practicality.
Beyond Interaction Patterns: Assessing Claims of Coordinated Inter-State Information Operations on Twitter/X
Authors: Valeria Pantè, David Axelrod, Alessandro Flammini, Filippo Menczer, Emilio Ferrara, Luca Luceri
Venue: WWW
Year: 2025

Social media platforms have become key tools for coordinated influence operations, enabling state actors to manipulate public opinion through strategic, collective actions. While previous research has suggested collaboration between states, such research failed to leverage state-of-the-art coordination indicators or control datasets. In this study, we investigate inter-state coordination by analyzing multiple online behavioral traces and using sophisticated coordination detection models. By incorporating a control dataset to differentiate organic user activity from coordinated efforts, our findings reveal no evidence of inter-state coordination. These results challenge earlier claims and underscore the importance of robust methodologies and control datasets in accurately detecting online coordination.
Welcome to the Dark Side: Analyzing the Revenue Flows of Fraud in the Online Ad Ecosystem
Authors: Emmanouil Papadogiannakis, Nicolas Kourtellis, Panagiotis Papadopoulos, Evangelos P. Markatos
Venue: WWW
Year: 2025

The online advertising market has recently reached the 500 billion dollar mark. To accommodate the need to match a user with the highest bidder at a fraction of a second, it has moved towards a complex, automated and often opaque model that involves numerous agents and intermediaries. Stimulated by the lack of transparency, but also the enormous potential profits, bad actors have found ways to circumvent restrictions, and generate substantial revenue that can support websites with objectionable or even illegal content. In this work, we evaluate transparency Web standards and show how shady actors take advantage of gaps to absorb ad revenues while putting the brand safety of advertisers in danger. We collect and study a large corpus of thousands of websites and show how ad transparency standards can be abused by bad actors to obscure ad revenue flows. We show how identifier pooling can redirect ad revenues from reputable domains to notorious domains serving objectionable content, and that the phenomenon is underestimated by previous studies by a factor of 15. Finally, we publish a Web monitoring service that enhances the transparency of supply chains and business relationships between publishers and ad networks.
Before & After: The Effect of EU's 2022 Code of Practice on Disinformation
Authors: Emmanouil Papadogiannakis, Panagiotis Papadopoulos, Nicolas Kourtellis, Evangelos P. Markatos
Venue: WWW
Year: 2025

Over the past few years, the European Commission has made significant steps to reduce disinformation in cyberspace. One of those steps has been the introduction of the 2022 ''Strengthened Code of Practice on Disinformation''. Signed by leading online platforms, this Strengthened Code of Practice on Disinformation is an attempt to combat disinformation on the Web. The Code of Practice includes a variety of measures including the demonetization of disinformation, urging, for example, advertisers ''to avoid the placement of advertising next to Disinformation content''. In this work, we set out to explore what was the impact of the Code of Practice and especially to explore to what extent ad networks continue to advertise on dis-/mis-information sites. We perform a historical analysis and find that, although at a hasty glance things may seem to be improving, there is really no significant reduction in the amount of advertising relationships among popular misinformation websites and major ad networks. In fact, we show that ad networks have withdrawn mostly from unpopular misinformation websites with very few visitors, but still form relationships with highly unreliable websites that account for the majority of misinformation traffic. To make matters worse, we show that ad networks continue to place advertisements of legitimate companies next to misinformation content. We show that major ad networks place ads in almost 400 misinformation websites in our dataset.
Customer Journey Mapping with Multimodal Large Language Models
Authors: Sravani Pati, Vamsi Krishna Pasam, Carlos Toxtli Hernandez
Venue: WWW
Year: 2025

The expansion of e-commerce requires accurate alignment of diverse multimodal content-such as user reviews, product images, and videos-with customer expectations. This study introduces a framework for mapping customer feedback to stages of the customer journey: Awareness, Consideration, Decision, and Post-Purchase. By analyzing user-generated content using Gemini 1.5 Flash, key features like sentiment, emotional tone, and product attributes were extracted to build a unified dataset. A hybrid classification approach was adopted, leveraging machine learning and rule-based logic. Among the models evaluated, Meta-LLaMA 3.2 3B and Meta-LLaMA 3.2-1B, large language models with robust contextual understanding, demonstrated the highest accuracy at 0.929, surpassing other models such as GPT-Neo, BERT, and RoBERTa. These results show how well the model captures complex customer opinion and aligns multimodal insights. The findings highlight discrepancies between consumer expectations and marketing narratives, particularly about the deliberation stage-a crucial phase in building trust. This research offers scalable and interpretable approaches to enhance customer journey mapping and improve content alignment in e-commerce.
Evaluating the Efficacy of Next.js: A Comparative Analysis with React.js on Performance, SEO, and Global Network Equity
Authors: Swostik Pati, Yasir Zaki
Venue: WWW
Year: 2025

This paper investigates the efficacy of Next.js as a framework addressing the challenges posed by React.js, particularly in performance, SEO, and equitable web accessibility. By constructing identical websites and web applications in both frameworks, we aim to evaluate the frameworks' behavior under diverse network conditions and capabilities. Beyond quantitative metrics like First Contentful Paint (FCP) and Time to Interactive (TTI), we incorporate qualitative user feedback to assess real-world usability. Our motivation stems from bridging the digital divide exacerbated by client-side rendering (CSR) frameworks and validating investments in modern technologies for businesses and institutions. Employing a novel LLM-assisted migration workflow, this paper also demonstrates the ease with which developers can transition from React.js to Next.js. Our results highlight Next.js's promise of better overall performance, without any degradation in user interaction experience, showcasing its potential to mitigate disparities in web accessibility and foster global network equity, thereby emerging as a compelling framework for shaping an inclusive web.
Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis
Authors: Changhua Pei, Zexin Wang, Fengrui Liu, Zeyan Li, Yang Liu, Xiao He, Rong Kang, Tieying Zhang, Jianjun Chen, Jianhui Li, Gaogang Xie, Dan Pei
Venue: WWW
Year: 2025

In the realm of microservices architecture, the occurrence of frequent incidents necessitates the employment of Root Cause Analysis (RCA) for swift issue resolution. It is common that a serious incident can take several domain experts hours to identify the root cause. Consequently, a contemporary trend involves harnessing Large Language Models (LLMs) as automated agents for RCA. Though the recent ReAct framework aligns well with the Site Reliability Engineers (SREs) for its thought-action-observation paradigm, its hallucinations often lead to irrelevant actions and directly affect subsequent results. Additionally, the complex and variable clues of the incident can overwhelm the model one step further. To confront these challenges, we propose Flow-of-Action, a pioneering Standard Operation Procedure (SOP) enhanced LLM-based multi-agent system. By explicitly summarizing the diagnosis steps of SREs, SOP imposes constraints on LLMs at crucial junctures, guiding the RCA process towards the correct trajectory. To facilitate the rational and effective utilization of SOPs, we design an SOP-centric framework called SOP flow. SOP flow contains a series of tools, including one for finding relevant SOPs for incidents, another for automatically generating SOPs for incidents without relevant ones, and a tool for converting SOPs into code. This significantly alleviates the hallucination issues of ReAct in RCA tasks. We also design multiple auxiliary agents to assist the main agent by removing useless noise, narrowing the search space, and informing the main agent whether the RCA procedure can stop. Compared to the ReAct method's 35.50% accuracy, our Flow-of-Action method achieves 64.01%, meeting the accuracy requirements for RCA in real-world systems.