OpenRaiser/Intern-Atlas huggingface dataset:
Boosting Asynchronous Decentralized Learning with Model Fragmentation
Authors: Sayan Biswas, Anne-Marie Kermarrec, Alexis Marouani, Rafael Pires, Rishi Sharma, Martijn de Vos
Venue: WWW
Year: 2025

Decentralized learning (DL) is an emerging technique that allows nodes on the web to collaboratively train machine learning models without sharing raw data. Dealing with stragglers, i.e., nodes with slower compute or communication than others, is a key challenge in DL. We present DivShare, a novel asynchronous DL algorithm that achieves fast model convergence in the presence of communication stragglers. DivShare achieves this by having nodes fragment their models into parameter subsets and send, in parallel to computation, each subset to a random sample of other nodes instead of sequentially exchanging full models. The transfer of smaller fragments allows more efficient usage of the collective bandwidth and enables nodes with slow network links to quickly contribute with at least some of their model parameters. By theoretically proving the convergence of DivShare, we provide, to the best of our knowledge, the first formal proof of convergence for a DL algorithm that accounts for the effects of asynchronous communication with delays. We experimentally evaluate DivShare against two state-of-the-art DL baselines, AD-PSGD and Swift, and with two standard datasets, CIFAR-10 and MovieLens. We find that DivShare with communication stragglers lowers time-to-accuracy by up to 3.9x compared to AD-PSGD on the CIFAR-10 dataset. Compared to baselines, DivShare also achieves up to 19.4% better accuracy and 9.5% lower test loss on the CIFAR-10 and MovieLens datasets, respectively.
Unearthing a Billion Telegram Posts about the 2024 U.S. Presidential Election: Development of a Public Dataset
Authors: Leonardo Blas, Luca Luceri, Emilio Ferrara
Venue: WWW
Year: 2025

With its lenient moderation and long-standing associations with potentially unlawful activities, Telegram has become an incubator for problematic content, frequently featuring conspiratorial, hyper-partisan, and fringe narratives. Exacerbating these concerns, investigations have underscored Telegram's role in organizing violent acts, such as those that occurred during the Capitol Hill attack on January 6, 2021. As of 2024, Telegram represents a focal arena for societal and political discourse, warranting close attention from the research community, regulators, and the media. Thus, we present a Telegram dataset focused on the 2024 US Presidential Election, comprising over 43 thousand chats and over 1 billion messages, and featuring chats' details, profile pictures, messages, and user information. In addition, we present application scenarios for this dataset, such as the construction of a network of chats, which can facilitate the analysis of interconnected communities, and a sample of harmful messages aimed at mobilizing supporters ahead of now-reported protests, which can serve as a case study to explore coordinated dynamics from the online to the offline world. This resource represents the largest public Telegram dataset to date, offering an unprecedented opportunity to study political discussion on Telegram.
Ten Years of Open Wikipedia Ranking
Authors: Paolo Boldi, Flavio Furia, Sebastiano Vigna
Venue: WWW
Year: 2025

The Open Wikipedia Ranking is an open dataset published yearly, containing the ranking of Wikipedia pages with respect to centrality measures computed on the whole Wikipedia graph for that year. In this paper, ten years after its start, we report some details, results and anecdotal observations on this dataset. The goal of the Open Wikipedia Ranking is to provide a completely open and reproducible ranking of Wikipedia pages based on indegree, PageRank, harmonic centrality, and page views; the Wikipedia graphs themselves are also made available by the Laboratory of Web Algorithmics. What characterizes the Open Wikipedia Ranking is that the whole graph construction and ranking process are meticulously documented and reproducible. All computations are based on open-source Java software and algorithms from the literature. Thus, the reason of the centrality score of pages can be exactly traced back to structural graph properties.
Synergizing Large Language Models and Knowledge-Based Reasoning for Interpretable Feature Engineering
Authors: Mohamed Bouadi, Arta Alavi, Salima Benbernou, Mourad Ouziri
Venue: WWW
Year: 2025

Feature engineering stands as a pivotal step in enhancing the performance of machine learning (ML) models, particularly with tabular data. However, traditional feature engineering methods are often time-consuming and requires case-by-case domain knowledge. In addition, as ML systems become more common, interpretability becomes increasingly important, especially among domain experts. To this end, we propose ReaGen, an automated feature engineering (AutoFE) approach that combines knowledge graphs (KGs) with large language models (LLMs) to generate interpretable features. ReaGen begins by symbolic REAsoning over the KG to extract relevant information based on datasets description. Then, it uses an LLM to iteratively GENerate meaningful features. Finally, to overcome challenges such as hallucinations and handling long contexts typical in LLMs, our model performs logical reasoning on the KG to ensure that the generated features maintain interpretability. ReaGen provides Python code for automatic feature generation and detailed explanations of feature utility. It leverages both LLM's internal knowledge and retrieved information from KGs. Experiments on public datasets demonstrate that ReaGen significantly improves prediction accuracy while ensuring high interpretability through human-like explanations for each feature. This work highlights the potential of integrating LLMs and KGs in feature engineering, paving the way for interpretable ML models.
Using Large Language Models for Simulating Social Networks
Authors: Azza Bouleimen
Venue: WWW
Year: 2025

Online platforms, especially social media, can present a set of challenges and harms to users such as toxicity, misinformation, and bullying. To mitigate this, social media companies implemented a number of intervention and moderation policies. However, research highlights the inefficiency, and sometimes the counter effects of these interventions. This PhD thesis work tackles this issues by proposing a different approach to designing intervention policies by simulating social media using Large Language Models.
Reducing Symbiosis Bias through Better A/B Tests of Recommendation Algorithms
Authors: Jennifer Brennan, Yahu Cong, Yiwei Yu, Lina Lin, Yajun Peng, Changping Meng, Ningren Han, Jean Pouget-Abadie, David M. Holtz
Venue: WWW
Year: 2025

It is increasingly common in digital environments to use A/B tests to compare the performance of recommendation algorithms. However, such experiments often violate the stable unit treatment value assumption (SUTVA), particularly SUTVA's ''no hidden treatments'' assumption, due to the shared data between algorithms being compared. This results in a novel form of bias, which we term ''symbiosis bias,'' where the performance of each algorithm is influenced by the training data generated by its competitor. In this paper, we investigate three experimental designs--cluster-randomized, data-diverted, and user-corpus co-diverted experiments--aimed at mitigating symbiosis bias. We present a theoretical model of symbiosis bias and simulate the impact of each design in dynamic recommendation environments. Our results show that while each design reduces symbiosis bias to some extent, they also introduce new challenges, such as reduced training data in data-diverted experiments. We further validate the existence of symbiosis bias using data from a large-scale A/B test conducted on a global recommender system, demonstrating that symbiosis bias affects treatment effect estimates in the field. Our findings provide actionable insights for researchers and practitioners seeking to design experiments that accurately capture algorithmic performance without bias in treatment effect estimates introduced by shared data.
Towards Agentic AI for Science Hypothesis Generation, Comprehension, Quantification, and Validation
Authors: Markus J. Buehler
Venue: WWW
Year: 2025

AI is revolutionizing scientific discovery by connecting seemingly unrelated fields - from mechanics to biology, and science to art. However, how can we build AI models that don't merely retrieve information but make new discoveries, going beyond interpolation to extrapolate to reason over never-before-seen scenarios and concepts? In this talk we describe how a new generation of physics-aware AI is breaking traditional boundaries through: Innovative graph-based generative AI combining physics and data-driven modeling Biologically-inspired neural structures that adapt dynamically Multi-agent systems that mirror natural systems Through practical case studies, I will present how this technology transforms materials science across scales - from silk and collagen to biomineralized materials - with direct applications in medicine, food systems, and agriculture. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis, is presented. The dynamic collaboration between agents, empowered by LLMs that can reason over sequences, data, images, and text, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study.
Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation
Authors: Tuan-Nghia Bui, Huy-Son Nguyen, Cam-Van Thi Nguyen, Hoang-Quynh Le, Duc-Trong Le
Venue: WWW
Year: 2025

Bundle recommendation aims to recommend a set of items to each user. However, the sparser interactions between users and bundles raise a big challenge, especially in cold-start scenarios. Traditional collaborative filtering methods do not work well for this kind of problem because these models rely on interactions to update the latent embedding, which is hard to work in a cold-start setting. We propose a new approach (DisCo), which relies on a personalized Diffusion backbone, enhanced by disentangled aspects for the user's interest, to generate a bundle in distribution space for each user to tackle the cold-start challenge. During the training phase, DisCo adjusts an additional objective loss term to avoid bias, a prevalent issue while using the generative model for top-K recommendation purposes. Our empirical experiments show that DisCo outperforms five comparative baselines by a large margin on three real-world datasets. Thereby, this study devises a promising framework and essential viewpoints in cold-start recommendation. Our materials for reproducibility are available at: https://github.com/bt-nghia/DisCo
Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals
Authors: Ruichu Cai, Zhifan Jiang, Kaitao Zheng, Zijian Li, Weilin Chen, Xuexin Chen, Yifan Shen, Guangyi Chen, Zhifeng Hao, Kun Zhang
Venue: WWW
Year: 2025

Multi-modal time series data is common in web technologies like the Internet of Things (IoT). Existing methods for multi-modal time series representation learning aim to disentangle the modality-shared and modality-specific latent variables. Although achieving notable performances on downstream tasks, they usually assume an orthogonal latent space. However, the modality-specific and modality-shared latent variables might be dependent on real-world scenarios. Therefore, we propose a general generation process, where the modality-shared and modality-specific latent variables are dependent, and further develop a Multi-modAl TEmporal Disentanglement (MATE) model. Specifically, our MATE model is built on a temporally variational inference architecture with the modality-shared and modality-specific prior networks for the disentanglement of latent variables. Furthermore, we establish identifiability results to show that the extracted representation is disentangled. More specifically, we first achieve the subspace identifiability for modality-shared and modality-specific latent variables by leveraging the pairing of multi-modal data. Then we establish the component-wise identifiability of modality-specific latent variables by employing sufficient changes of historical latent variables. Extensive experimental studies on 12 datasets show a general improvement in different downstream tasks, highlighting the effectiveness of our method in real-world scenarios.
Dynamic Security Analysis of JavaScript: Are We There Yet?
Authors: Stefano Calzavara, Samuele Casarin, Riccardo Focardi
Venue: WWW
Year: 2025

In this paper, we systematically evaluate the effectiveness of existing tools for the dynamic security analysis of client-side JavaScript, focusing in particular on information flow control. Each tool is evaluated in terms of: (i) compatibility, i.e., the ability to process and analyze existing scripts without breaking; (ii) transparency, i.e., the ability to preserve the original script semantics when security enforcement is not necessary; (iii) coverage, i.e., the effectiveness in terms of number of detected information flows; (iv) performance, i.e., the computational overhead introduced by the analysis. Our investigation shows that most of the existing analysis tools are incompatible with the modern Web and the compatibility issues affecting them are not easily fixed. Moreover, transparency issues abound and make us question analysis correctness. This is also confirmed by our coverage evaluation, showing that some tools are unable to detect any information flow on real-world websites, while the remaining tools report significantly different outputs. Finally, we observe that the computational overhead of analysis tools may be significant and can exceed 30x. In the end, out of all the evaluated tools, just one of them (Project Foxhound) is effective enough for practical adoption at scale.
Witty Gerbil Chrome Extension: A Browser-Based Framework for Comprehensive AI System Evaluation
Authors: Samuel Cameron
Venue: WWW
Year: 2025

Many AI red-teaming frameworks target Large Language Models (LLMs) and other generative systems via direct access or through APIs. These approaches do not always reflect the complexities of real-world deployments. Production AI applications often incorporate content moderation, guardrails, user interface constraints, and other filtering mechanisms, which can alter both user inputs and system outputs. To capture the full range of vulnerabilities, we present Witty Gerbil Chrome Extension, a browser-based testing solution coupled with a Python orchestration backend. By automating AI interactions directly in the browser, our framework preserves production safeguards and transforms, providing a more realistic picture of overall system risk. This paper outlines the architecture, operation modes, and limitations of this extension, emphasizing the importance of holistic AI evaluations that include user-facing layers in addition to core model testing.
MAP the Blockchain World: A Trustless and Scalable Blockchain Interoperability Protocol for Cross-chain Applications
Authors: Yinfeng Cao, Jiannong Cao, Dongbin Bai, Long Wen, Yang Liu, Ruidong Li
Venue: WWW
Year: 2025

Blockchain interoperability protocols enable cross-chain asset transfers or data retrievals between isolated chains, which are considered as one of the core infrastructure for Web 3.0. However, existing protocols either face severe scalability issues due to high on-chain and off-chain cost, or suffer from trust concerns because of centralized architecture. In this paper, we propose MAP, a trustless blockchain interoperability protocol that relays cross-chain transactions across heterogeneous chains with high scalability. First, within MAP, we develop a novel cross-chain relay architecture, which integrates a unified relay chain and on-chain light clients of source chains, allowing the trustworthy retrieval and verification of heterogeneous cross-chain transactions. Furthermore, we reduce cross-chain verification cost by incorporating an optimized on-chain light client scheme that adaptively decouples signature verification overheads from inefficient smart contract execution and offloads them to off-chain provers. For experiments, we conduct the first large-scale evaluation on existing blockchain interoperability protocols. With MAP, the required number of on-chain light clients is reduced from O(N2) to O(N), with decreasing on-chain cost 35% and 25% off-chain cost when verifying cross-chain transactions. To demonstrate the effectiveness, we deployed MAP in the real world. By 2025, we have supported over six popular public chains, 50 cross-chain applications and relayed over 200K cross-chain transactions worth over 640 million USD. Based on the deployment records, we construct the first real-world cross-chain dataset to further advance blockchain interoperability research.
Hyperbolic-Euclidean Deep Mutual Learning
Authors: Haifang Cao, Yu Wang, Jialu Li, Pengfei Zhu, Qinghua Hu
Venue: WWW
Year: 2025

Graph neural networks (GNNs) exhibit powerful performance in handling graph data, with Euclidean and hyperbolic variants excelling in processing grid-based and hierarchical structures, respectively. However, existing methods focus on learning specific structures linked to the inherent properties of the underlying space, failing to fully exploit their complementary properties in distinct geometric spaces, thus limiting their ability to efficiently model complex graph structures. In this paper, we propose a Hyperbolic-Euclidean Deep Mutual Learning (H-EDML) framework, which leverages the unique properties of hyperbolic space to effectively capture the hierarchical relationships present in graph data, while also utilizes the familiar Euclidean space to handle local interactions. Specifically, We design a topology mutual learning module to bolster the capacity of each single model to perceive the holistic topological structure of the graph. Then, we integrate a decision mutual learning module to further advance the models' comprehensive judgment capabilities towards graph data, thereby strengthening the robustness and generalization. Furthermore, we employ an attention-based probabilistic integration strategy for the final prediction to alleviate potential disparities in decision-making among different models. Extensive experiments on node classification are conducted on five real-world graph datasets and the results show that our proposed H-EDML achieves competitive performances compared to the state-of-the-art methods. The source code will be available at: https://github.com/caohaifang123/H-EDML.
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Authors: Yuwei Cao, Liangwei Yang, Zhiwei Liu, Yuqing Liu, Chen Wang, Yueqing Liang, Hao Peng, Philip S. Yu
Venue: WWW
Year: 2025

Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. To enable positive knowledge transfer, we design a loss function that enforces alignment and uniformity both within and across submodules. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone and achieves state-of-the-art results. Our implementations are publicly available at https://github.com/YuweiCao-UIC/GSAU.git
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems
Authors: Yang Cao, Changhao Zhang, Xiaoshuang Chen, Kaiqiao Zhan, Ben Wang
Venue: WWW
Year: 2025

Recommender systems need to optimize various types of user feedback, e.g., clicks, likes, and shares. A typical recommender system handling multiple types of feedback has two components: a multi-task learning (MTL) module, predicting feedback such as click-through rate and like rate; and a multi-task fusion (MTF) module, integrating these predictions into a single score for item ranking. MTF is essential for ensuring user satisfaction, as it directly influences recommendation outcomes. Recently, reinforcement learning (RL) has been applied to MTF tasks to improve long-term user satisfaction. However, existing RL-based MTF methods are formula-based methods, which only adjust limited coefficients within pre-defined formulas. The pre-defined formulas restrict the RL search space and become a bottleneck for MTF. To overcome this, we propose a formula-free MTF framework. We demonstrate that any suitable fusion function can be expressed as a composition of single-variable monotonic functions, as per the Sprecher Representation Theorem. Leveraging this, we introduce a novel learnable monotonic fusion cell (MFC) to replace pre-defined formulas. We call this new MFC-based model eXtreme MTF (xMTF). Furthermore, we employ a two-stage hybrid (TSH) learning strategy to train xMTF effectively. By expanding the MTF search space, xMTF outperforms existing methods in extensive offline and online experiments.
Empirical Evaluation of Prompting Strategies for Fact Verification Tasks
Authors: Mohna Chakraborty, Adithya Kulkarni, Qi Li
Venue: WWW
Year: 2025

Commercial large language models (LLMs) such as GPT-3.5 have emerged as powerful tools for diverse natural language processing (NLP) tasks, yet concerns persist about their reliability in generating factual responses. This study investigates the potential of commercial LLMs such as GPT-3.5 for fact verification, addressing three key research questions: (1) Can GPT-3.5 perform fact verification reliably? (2) How do different prompting strategies affect their performance? (3) What are the common errors they make with the most effective prompt? Using the benchmark FEVER 1.0 dataset, we designed and evaluated three prompts, with experiments conducted using GPT-3.5 as a representative commercial LLM. Our experiments demonstrate that GPT-3.5 achieves a Label Accuracy (LA) of over 72% with the best-performing prompt, significantly outperforming simpler prompts. A detailed error analysis reveals that approximately 70% of mistakes stem from logical reasoning and contextual misunderstandings. These findings suggest that carefully crafted prompts can substantially enhance the accuracy of LLMs, in fact verification tasks, highlighting their potential as supportive tools for applications in sensitive domains.
BrickMIR: A Minimal, Imbalance-tuned, and Ratio-based Framework for Brick Metadata Classification
Authors: Jun Hao Chan
Venue: WWW
Year: 2025

We propose a multi-label time-series classification framework for building metadata labeling. Rather than relying on absolute values (which vary widely across different buildings), we focus on ratio- and correlation-based features that capture each sensor's relative behavior. Along with minimal data cleaning and labeltuned class weights, this approach generalizes well across buildings with differing baselines, as demonstrated on the Brick by Brick 2024 dataset. Source code is publicly available at https://github.com/chanjunha0/BrickMIR.