OpenRaiser/Intern-Atlas huggingface dataset:
Graph Self-Supervised Learning with Learnable Structural and Positional Encodings
Authors: Asiri Wijesinghe, Hao Zhu, Piotr Koniusz
Venue: WWW
Year: 2025

Traditional Graph Self-Supervised Learning (GSSL) struggles to capture complex structural properties well. This limitation stems from two main factors: (1) the inadequacy of conventional Graph Neural Networks (GNNs) in representing sophisticated topological features, and (2) the focus of self-supervised learning solely on final graph representations. To address these issues, we introduce GenHopNet, a GNN framework that integrates a k-hop message-passing scheme, enhancing its ability to capture local structural information without explicit substructure extraction. We theoretically demonstrate that GenHopNet surpasses the expressiveness of the classical Weisfeiler-Lehman (WL) test for graph isomorphism. Furthermore, we propose a structural- and positional-aware GSSL framework that incorporates topological information throughout the learning process. This approach enables the learning of representations that are both sensitive to graph topology and invariant to specific structural and feature augmentations. Comprehensive experiments on graph classification datasets, including those designed to test structural sensitivity, show that our method consistently outperforms the existing approaches and maintains computational efficiency. Our work significantly advances GSSL's capability in distinguishing graphs with similar local structures but different global topologies.
Fact-based Counter Narrative Generation to Combat Hate Speech
Authors: Brian Wilk, Homaira Huda Shomee, Suman Kalyan Maity, Sourav Medya
Venue: WWW
Year: 2025

Online hatred has become an increasingly pervasive issue, affecting individuals and communities across various digital platforms. To combat hate speech in such platforms, counter narratives (CNs) are regarded as an effective method. In recent years, there has been growing interest in using generative AI tools to construct CNs. However, most of the generative models produce generic responses to hate speech and can hallucinate, reducing their effectiveness. To address the above limitations, we propose a counter narrative generation method that enhances CNs by providing non-aggressive, fact-based narratives with relevant background knowledge from two distinct sources, including a web search module. Furthermore, we conduct a comprehensive evaluation using multiple metrics, including LLM-based measures for persuasion, factuality, and informativeness, along with human and traditional NLP evaluations. Our method significantly outperforms baselines, achieving an average factuality score of 0.915, compared to 0.741, 0.701, and 0.69 for competitive baselines, and performs well in human evaluations.
Improving LLM-Based Recommender Systems with User-Controllable Profiles
Authors: Stanislaw Wozniak, Jacek Duszenko, Jan Kocon, Przemyslaw Kazienko
Venue: WWW
Year: 2025

Large Language Models (LLMs) have demonstrated significant potential across various domains, including their application in recommendation systems (RS). In this paper, we propose a method that emphasizes user control, thereby increasing the role of the human within the system. Our research investigates the effectiveness of a variety of LLMs in capturing and using user preferences for recommendation tasks. The findings reveal that incorporating user controllability into RS can enhance performance by up to 50%. Furthermore, the results highlight that textual and user-controlled representations of preferences, called user-controllable profiles, outperform historical data to improve recommendation quality.
WBSan: WebAssembly Bug Detection for Sanitization and Binary-Only Fuzzing
Authors: Xiao Wu, Junzhou He, Liyan Huang, Cai Fu, Weihang Wang
Venue: WWW
Year: 2025

With the advancement of WebAssembly, abbreviated as Wasm, various memory bugs and undefined behaviors have emerged, leading to security issues that affect usability and portability. Existing methods struggle to detect these problems in Wasm binaries due to challenges associated with binary instrumentation and the difficulty of defining legal memory bounds.While sanitizers combined with fuzzing are recognized as effective means for identifying bugs, current Wasm sanitizers necessitate compile-time instrumentation, rendering them unsuitable for practical scenarios where only binaries are accessible. In this paper, we propose WBSan, the first Wasm binary sanitizer employing static analysis and Wasm binary instrumentation to detect memory bugs and undefined behaviors. We develop distinct instrumentation patterns tailored for each type of bug and introduce Wasm shadow memory to address complex memory bugs. Our results reveal that WBSan achieves a 16.8% false detection rate, outperforming current Wasm binary checkers and native sanitizers in detecting memory bugs and undefined behaviors. Furthermore, when compared with the binary-only fuzzer, WBSan uncovers more crashes and achieves greater code coverage.
From Literature to Lab: Hardware-Independent Autonomous Chemical Synthesis with Reinforcement Learning
Authors: Junfeng Wu, Jing He, Hai Liu, Zhaoqi Zheng, Yichen Cao, Xingguo Chen, Bingjie Zou, Ruiping Zou, Guohua Zhou, David Sturgess, André Van Zundert
Venue: WWW
Year: 2025

Chemical synthesis, a fundamental process in chemical engineering, traditionally requires extensive manual intervention and expertise, particularly in interpreting scientific literature and translating it into executable workflows. Autonomous systems offer the potential to revolutionize this field by enabling robots to autonomously read scientific literature and form general synthesis workflows. However, a key challenge lies in integrating diverse hardware components, requiring adaptable architectures that can seamlessly operate across various platforms. Furthermore, these systems must be robust, allowing for real-time error correction, and accessible to non-programmers. This paper presents Autonomous Chemical Synthesis System (ACSS), an adaptable architecture for chemical execution systems designed to address these challenges. ACSS enables robots to autonomously read scientific literature and form general synthesis workflows. We achieve hardware independence through protocol-integration reinforcement learning, enabling seamless integration across platforms. Chemical code and hardware descriptions are compiled and converted into robotic instructions for execution. Natural language error correction allows non-programmers to easily adjust the system. Our key contributions are: (1) Literature to Lab: directly extracting synthesis protocols from scientific articles; (2) Hardware Independence: developing an adaptive, reinforcement learning-based model for diverse hardware setups. ACSS has successfully synthesized 12 diverse compounds from literature, including lidocaine (a painkiller), Dess-Martin periodinane (an oxidizing agent), and alkyl fluoride (a fluorinating agent). This demonstration highlights the potential of NLP and reinforcement learning for efficient and accessible chemical research and production. Click here to find out more on YouTube.
Aegis: Post-Training Attribute Unlearning in Federated Recommender Systems against Attribute Inference Attacks
Authors: Wenhan Wu, Jiawei Jiang, Chuang Hu
Venue: WWW
Year: 2025

As privacy concerns in recommender systems become increasingly prominent, federated recommender systems (FedRecs) have emerged as a promising distributed training paradigm. FedRecs enable the collaborative training of a shared global recommendation model without requiring the exchange of raw client interaction data. However, models trained using standard FedRec methods remain vulnerable to personal information leakage, particularly through attribute inference attacks, which can expose sensitive user attributes such as gender and race. In this paper, we address these user-sensitive attributes as targets for federated unlearning. To protect users' sensitive information, attribute unlearning aims to eliminate sensitive attributes from user embeddings, thereby preventing inference attacks while preserving recommendation performance. We introduce a novel post-training federated unlearning framework, Aegis, which performs unlearning based on private attribute requests after the model has been trained, minimizing the degradation in recommendation accuracy. Aegis employs an information-theoretic multi-component loss function to balance privacy protection and recommendation performance. Additionally, Aegis adapts to scenarios where training interaction data may be unavailable, reflecting real-world centralized protection scenarios. Comprehensive evaluations of various benchmark datasets demonstrate that our proposed method effectively safeguards user privacy while maintaining high-quality recommendations.
Safeguarding Blockchain Ecosystem: Understanding and Detecting Attack Transactions on Cross-chain Bridges
Authors: Jiajing Wu, Kaixin Lin, Dan Lin, Bozhao Zhang, Zhiying Wu, Jianzhong Su
Venue: WWW
Year: 2025

Cross-chain bridges are essential decentralized applications (DApps) to facilitate interoperability between different blockchain networks. Unlike regular DApps, the functionality of cross-chain bridges relies on the collaboration of information both on and off the chain, which exposes them to a wider risk of attacks. According to our statistics, attacks on cross-chain bridges have resulted in losses of nearly 4.3 billion since 2021. Therefore, it is particularly necessary to understand and detect attacks on cross-chain bridges. In this paper, we collect the largest number of cross-chain bridge attack incidents to date, including 49 attacks that occurred between June 2021 and September 2024, of which 22 were attacks on cross-chain bridge business logic. Our analysis reveal that attacks against cross-chain business logic cause significantly more damage than those that do not. These cross-chain attacks exhibit different patterns compared to normal transactions in terms of call structure, which effectively indicates potential attack behaviors. Given the significant losses in these cases and the scarcity of related research, this paper aims to detect attacks against cross-chain business logic, and propose the BridgeGuard tool. Specifically, BridgeGuard models cross-chain transactions from a graph perspective, and employs a two-stage detection framework comprising global and local graph mining to identify attack patterns in cross-chain transactions. We conduct multiple experiments on the datasets with 203 attack transactions and 40,000 normal cross-chain transactions. The results show that BridgeGuard's reported recall score is 36.32% higher than that of state-of-the-art tools and can detect unknown attack transactions.
Leveraging ChatGPT to Empower Training-free Dataset Condensation for Content-based Recommendation
Authors: Jiahao Wu, Qijiong Liu, Hengchang Hu, Wenqi Fan, Shengcai Liu, Qing Li, Xiao-Ming Wu, Ke Tang
Venue: WWW
Year: 2025

Modern Content-Based Recommendation (CBR) techniques utilize item content to deliver personalized services, effectively mitigating information overload. However, these methods often require resource-intensive training on large datasets. To address this issue, we explore dataset condensation for textual CBR in this paper. Dataset condensation aims to synthesize a compact yet informative dataset, enabling models to achieve performance comparable to those trained on full datasets. Applying existing approaches to CBR presents two key challenges: (1) the difficulty of synthesizing discrete texts and (2) the inability to preserve user-item preference information. To overcome these limitations, we propose TF-DCon, an efficient dataset condensation method for CBR. TF-DCon employs a prompt-evolution module to guide ChatGPT in condensing discrete texts and integrates a clustering-based module to condense user preferences effectively. Extensive experiments conducted on three real-world datasets demonstrate TF-DCon's effectiveness. Notably, we are able to approximate up to 97% of the original performance while reducing the dataset size by 95% (i.e., dataset MIND). We have released our code and data for other researchers to reproduce our results.
Token Graph Generation for Robust Named Entity Recognition
Authors: Wenjia Wu, Shuzi Niu, Ziwei Li, Tao Yuan, Weijia Xu
Venue: WWW
Year: 2025

Named Entity Recognition (NER) is to extract pieces from text and classify them into predefined categories. For most state-of-the-art NER methods, the attention mechanism is a core component of their network architectures. Meanwhile, it is often blamed for introducing of spurious token correlations, which do harm to the prediction accuracy. To make the NER model robust to such noisy token relations, we focus on how to remove these relations from the complete token graph and identify the actually useful token graph for NER. Starting with a complete token graph from the self-attention matrix, we propose a token graph generated method to explicitly model the core token graph construction process by deleting edges iteratively in a reinforcement learning framework. The Generated Token Graph, denoted as GTG, can be utilized in two primary ways: as a masking matrix to serve as an adapter for attention mechanism, namely GTG-adapter. Experimental results on NER benchmark datasets show our proposed method will improve the accuracy and robustness of NER models compared with SOTA baselines.
Brewing Vodka: Distilling Pure Knowledge for Lightweight Threat Detection in Audit Logs
Authors: Weiheng Wu, Wei Qiao, Wenhao Yan, Bo Jiang, Yuling Liu, Baoxu Liu, Zhigang Lu, Junrong Liu
Venue: WWW
Year: 2025

Advanced Persistent Threats (APTs) are continuously evolving, leveraging their stealthiness and persistence to put increasing pressure on current provenance-based Intrusion Detection Systems (IDS). This evolution exposes several critical issues: (1) The dense interaction between malicious and benign nodes within provenance graphs introduces neighbor noise, hindering effective detection; (2) The complex prediction mechanisms of existing APTs detection models lead to the insufficient utilization of prior knowledge embedded in the data; (3) The high computational cost makes detection impractical. To address these challenges, we propose Vodka, a lightweight threat detection system built on a knowledge distillation framework, capable of node-level detection within audit log provenance graphs. Specifically, Vodka applies graph Laplacian regularization to reduce neighbor noise, obtaining smoothed and denoised graph signals. Subsequently, Vodka employs a teacher model based on GNNs to extract knowledge, which is then distilled into a lightweight student model. The student model is designed as a combination of a feature transformation module and a personalized PageRank random walk label propagation module, with the former capturing feature knowledge and the latter learning label and structural knowledge. After distillation, the student model benefits from the knowledge of the teacher model to perform precise threat detection. Finally, Vodka reconstructs attack paths from anomalous nodes, providing insight into the attackers' strategies. We evaluate Vodka through extensive experiments on three public datasets and compare its performance against several state-of-the-art IDS solutions. The results demonstrate that Vodka achieves outstanding detection accuracy across all scenarios.
CLEAR: Climate Policy Retrieval and Summarization Using LLMs
Authors: Yutao Wu, Kun Ran, Ming Liu, Adam P. A. Cardilini, Arif Nurwidyantoro, Xiao Liu
Venue: WWW
Year: 2025

Web platforms play a critical role in climate change communication. However, despite facing increasing climate issues, residents especially those living in rural areas still struggle to access and interpret climate policies, while local governments lack structured feedback on policy effectiveness and community needs. To bridge this dual communication challenge, we present CLEAR (Climate PoLicy rEtrieval and summA Rization), a system powered by Large Language Models (LLMs) which can analyze resident queries and provide customized responses addressing their climate concerns based on relevant local government policy documents. Our system leverages a fine-tuned Llama-3.2-3B to decompose natural queries into structured components to enable precise information retrieval and summarization. The workflow of the CLEAR system consists of: (1) semantic query analysis to identify resident location and climate concerns, (2) intelligent retrieval from a curated dataset of authoritative policy documents, and (3) multi-modal policy summarization with geospatial context. When there is no corresponding policy for the specific climate concern, the CLEAR system can help to generate contextualized feedback to relevant local governments. In this paper, through real-world implementation and demonstration, we show how CLEAR can effectively bridge the information gap between Australian rural communities and local governments through LLM-based policy retrieval and summarization. Our source code is available at https://github.com/wuyoscar/CLEAR.
Blockchain-based Framework for Scalable and Incentivized Federated Learning
Authors: Bijun Wu, Oshani Seneviratne
Venue: WWW
Year: 2025

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems often rely on centralized aggregating mechanisms, introducing trust issues, single points of failure, and limited mechanisms for incentivizing meaningful client contributions. These challenges are exacerbated as FL scales to train resource-intensive models, such as large language models (LLMs), requiring scalable, decentralized solutions. This paper presents a blockchain-based FL framework that addresses these limitations by integrating smart contracts and a novel hybrid incentive mechanism. The framework automates critical FL tasks, including client registration, update validation, reward distribution, and maintaining a transparent global state. The hybrid incentive mechanism combines on-chain alignment-based rewards, off-chain fairness checks, and consistency multipliers to ensure fairness, transparency, and sustained engagement. We evaluate the framework through gas cost analysis, demonstrating its feasibility for different scales of federated learning scenarios.
Image Fusion for Cross-Domain Sequential Recommendation
Authors: Wangyu Wu, Siqi Song, Xianglin Qiu, Xiaowei Huang, Fei Ma, Jimin Xiao
Venue: WWW
Year: 2025

Cross-Domain Sequential Recommendation (CDSR) aims to predict future user interactions based on historical interactions across multiple domains. The key challenge in CDSR is effectively capturing cross-domain user preferences by fully leveraging both intra-sequence and inter-sequence item interactions. In this paper, we propose a novel method, Image Fusion for Cross-Domain Sequential Recommendation (IFCDSR), which incorporates item image information to better capture visual preferences. Our approach integrates a frozen CLIP model to generate image embeddings, enriching original item embeddings with visual data from both intra-sequence and inter-sequence interactions. Additionally, we employ a multiple attention layer to capture cross-domain interests, enabling joint learning of single-domain and cross-domain user preferences. To validate the effectiveness of IFCDSR, we re-partitioned four e-commerce datasets and conducted extensive experiments. Results demonstrate that IFCDSR significantly outperforms existing methods.
Personalized Recommendation Agents with Self-Consistency
Authors: Zijing Wu, Leheng Sheng, Yuanlin Xia, Yi Zhang, Yuxin Chen, An Zhang
Venue: WWW
Year: 2025

This paper presents the 2nd place solution of the WWW'25 AgentSociety Challenge (Recommendation Track), which aims to advance the development of Large Language Model (LLM)-based agents for personalized recommendation. During this challenge, participants are tasked with building LLM-based agents capable of ranking items according to user preferences. To evaluate the practical capabilities of these agents, the organizer provides a robust evaluation platform that faithfully simulates real-world recommendation scenarios across multiple domains (e.g., Yelp, Goodreads, and Amazon). To tackle this challenge, our team, RecHackers, develops an agent-voting system inspired by the idea that self-consistency across multiple LLM outputs can enhance the accuracy. Specifically, our approach follows a prompting-sampling-voting paradigm. We first carefully design prompts for individual agents based on user interaction and item characteristics. Then, we query LLM-based agents multiple times with a controlled temperature to obtain a diverse range of candidate rankings. Lastly, we aggregate these rankings with predefined voting mechanisms, such as majority voting or Borda count, to derive the final ranking list. This simple yet effective solution achieved the 2nd place in the WWW'25 AgentSociety Challenge (Recommendation Track).
ODNS Clustering: Unveiling Client-Side Dependency in Open DNS Infrastructure
Authors: Wenhao Wu, Zhaohua Wang, Qinxin Li, Zihan Li, Yi Li, Jin Yan, Zhenyu Li
Venue: WWW
Year: 2025

There are over a million open DNS servers in the wild. However, not all servers perform recursive queries directly. Instead, many DNS forwarders forward queries to upstream recursive servers or other DNS forwarders for name resolving on their behalf. The groups of open servers that have such dependencies on each other form ODNS Clusters. The dependencies can result in vulnerabilities; yet we have little knowledge of the ODNS cluster structure. In this work, we measure the inter-dependence of open DNS resolvers and find that 1.9 million open DNS servers form only 81,636 ODNS clusters. We further analyze the characteristics of the clustered ODNS structure. The key observations include biased cluster size distribution, discrepancy of ODNS infrastructures among countries, concentration in major public DNS server providers, and potential security and resilience risks due to the dependence.
Hunting in the Dark Forest: A Pre-trained Model for On-chain Attack Transaction Detection in Web3
Authors: Zhiying Wu, Jiajing Wu, Hui Zhang, Zibin Zheng, Weiqiang Wang
Venue: WWW
Year: 2025

In recent years, a large number of on-chain attacks have emerged in the blockchain empowered Web3 ecosystem. In the year of 2023 alone, on-chain attacks have caused losses of over 585 million. Attackers use blockchain transactions to carry out on-chain attacks, for example, exploiting vulnerabilities or business logic flaws in Web3 applications. A wealth of efforts have been devoted to detecting on-chain attack transactions through expert patterns and machine learning techniques. However, in this ever-evolving ecosystem, the performance of current methods is limited in detecting new on-chain attacks, due to the obsoleting of attack recognition patterns or the reliance on on-chain attack samples. In this paper, we propose a universal approach for detecting on-chain attacks even when there are few or even no new on-chain attack samples. Specifically, an in-depth analysis of the transaction characteristics is conducted, and we propose a new insight to train a generic attack transaction detecting model, i.e., transaction reconstruction. Particularly, to overcome the over-fitting in the transaction reconstruction task, we use the web-scale function comments related to transactions as supervision information, rather than expert-confirmed labels. Experimental results demonstrate that the proposed approach surpasses the supervised state-of-the-art by 13% in AUC, with just 30 known on-chain attack samples. Moreover, without any known attack samples, our method can still detect new on-chain attacks in the wild (with a precision of 61.83%). Among attacks detected in the wild, we confirm 1,692 address poisoning attacks, a new type of on-chain attack targeting token holders. Our code is available at: https://github.com/wuzhy1ng/attack_trans_detection_www25.