OpenRaiser/Intern-Atlas huggingface dataset:
CBRCL: A CLIP-BERT with Retrieval-Guided Contrastive Learning Multimodal Approach for Crisis-Driven Hate Speech Detection
Authors: Ilya Stepanov, Junaid Rashid, Jong Weon Lee, Salman Naseem, Imran Razzak
Venue: WWW
Year: 2025

The proliferation of multimodal text and image data has raised the challenge of detecting hate speech, a critical issue in mitigating the spread of hate and propaganda on social media. The challenge in detecting hateful posts arises when the combination of images and text, which individually lack hateful meaning, collectively convey hatefulness. To address this problem, we propose CBRCL, a novel approach leveraging CLIP and BERT for embedding generation and retrieval-guided contrastive learning to enhance hate speech detection. Our approach is evaluated on two datasets: CrisisHateMM, comprising text-embedded images associated with the Ukraine-Russia conflict, and LoveHate, focused on text-embedded images from the Israel-Palestine conflict. Experimental results demonstrate that CBRCL outperforms both unimodal and multimodal baselines, achieved an accuracy of 86.4% on CrisisHateMM and 76.3% on LoveHate datasets. By combining CLIP's strong visual-text alignment with BERT's deep contextual understanding, CBRCL effectively captures subtle multimodal hate speech. Additionally, retrieval-guided contrastive learning refines embedding spaces, ensuring a clearer distinction between hateful and non-hateful content.
Multimodal Knowledge Graph Error Detection with Disentanglement VAE and Multi-Grained Triplet Confidence
Authors: Xuhui Sui, Ying Zhang, Yu Zhao, Baohang Zhou, Xiaojie Yuan
Venue: WWW
Year: 2025

Multimodal knowledge graphs inevitably contain numerous errors due to the lack of human supervision in their automated construction and updating processes. These errors can significantly degrade the performance of downstream applications that rely on them. Existing researches on knowledge graph error detection primarily focus on leveraging graph structural and textual information to identify triplet errors in unimodal knowledge graphs. However, unlike unimodal knowledge graphs, multimodal knowledge graphs also suffer from mismatches between images and their corresponding entities, referred to as modality errors. These modality errors not only hinder the performance of downstream applications but also impede our effective utilization of the abundant complementary information provided by the visual modality for detecting triplet errors. To this end, we introduce a novel task of multimodal knowledge graph error detection (MKGED) in this paper, aiming at simultaneously identifying both modality errors and triplet errors. Given the lack of datasets for evaluating this task, we first establish two comprehensive MKGED datasets. Furthermore, we propose a novel framework, KGDMC, to address the MKGED task. Within KGDMC, we devise a disentanglement modality reconstruction (DMR) module for modality error detection. This module disentangles each original modality representation into two disjoint components: modality-specific representations and modality-invariant representations, leveraging the cross-modality reconstruction process to detect mismatched visual modalities. Additionally, for the triplet error detection, we propose a multi-grained triplet confidence (MTC) module, incorporating local triplet confidence, global structure confidence, and global path confidence, to collaboratively detect mismatched triplets. Extensive experiments on our constructed two datasets demonstrate the superiority of our proposed framework.
Model-Agnostic Social Network Refinement with Diffusion Models for Robust Social Recommendation
Authors: Youchen Sun, Zhu Sun, Yingpeng Du, Jie Zhang, Yew Soon Ong
Venue: WWW
Year: 2025

Social recommendations (SRs) aim to enhance preference modeling by integrating social networks. However, their effectiveness is mainly constrained by two factors: the noisy social connections that may not reflect shared interests, and the limited number of social connections for most users, which hampers the system's ability to fully leverage social influence. Therefore, it is essential to perform social network refinement by removing noisy connections and adding meaningful ones for robust SRs. Inspired by the denoising capability of generative diffusion models, we propose a Model-Agnostic Social Network Refinement framework with Diffusion Models for Robust Social Recommendation (ARD-SR). Specifically, in the forward process, we corrupt the social network by progressively adding position-specific Gaussian noise calibrated to the user preference similarity, better simulating how the social network responds to noise perturbations. The reverse process learns to denoise, guided by each user's neighborhood preferences from the SR backbone, generating a tailored social network aligned with each user's preference for establishing connections. For effective learning, we design a curriculum-based training mechanism that progressively introduces challenging samples characterized by high sparsity or high noise levels. Finally, ARD-SR and the SR backbone are alternately trained, ensuring a continuous mutual enhancement between the social network refinement and the backbone's user representation learning. To further enhance the quality of the refined social network, (1) we introduce a preference-guided flip operation during inference to improve the input quality; and (2) we modify social connections based on the exponential weighted moving average of ARD-SR's predictions across epochs to reduce fluctuations. Experiments on three datasets show that ARD-SR significantly improves SR performance across multiple SR backbones. The code is released at https://github.com/sunyc123r/ARD-SR.
Multimodal Taylor Series Network for Misinformation Detection
Authors: Jiahao Sun, Chen Chen, Chunyan Hou, Yike Wu, Xiaojie Yuan
Venue: WWW
Year: 2025

With the rapid development of the Internet and the widespread use of social media, the proliferation of multimodal misinformation combining images and text poses serious risks to societal trust, individual well-being, and the integrity of AI models trained on such data. Recently, the automatic detection multimodal misinformation has become an essential area of research. However, traditional methods often rely on hierarchical neural networks that compress and fuse modalities, potentially overlooking deeper interactions between modalities and reducing model interpretability. In this paper, we present a novel Multimodal Taylor Series (MTS) network for detecting multimodal misinformation. The MTS network leverages Taylor series expansion to explicitly capture both low-order and high-order interactions between modalities, which also enhances interpretability by decomposing the model's processing into distinct terms. Additionally, the proposed MTS network avoids exponential parameter growth and maintains linear scalability, allowing the model to effectively capture complex cross-modal correlations. Extensive experiments on three benchmark datasets demonstrate that the MTS network significantly outperforms state-of-the-art models. We have open-sourced the code and logs at: https://github.com/OneForAllSama/MTS.
Scalable Estimation of Exponential Random Graph Models on Large Networks
Authors: Yidan Sun
Venue: WWW
Year: 2025

Many scientific fields study large networks with millions of nodes and edges to identify structural and relational patterns. Exponential Random Graph Models (ERGMs) offer a powerful statistical framework for analyzing network formation by estimating the probability distribution over networks based on local structural features and nodal attributes. However, their application to large networks is hindered by the computational challenges inherent in traditional estimation methods. This research introduces a novel 'divide-and-conquer' approach to partition large networks into smaller, overlapping sub-networks. By estimating ERGMs on these sub-networks and then merging the results, this thesis aims to overcome scalability issues while preserving both local and global network structures.
Enhancing Product Search Interfaces with Sketch-Guided Diffusion and Language Agents
Authors: Edward Sun
Venue: WWW
Year: 2025

The rapid progress in diffusion models, transformers, and language agents has unlocked new possibilities, yet their potential in user interfaces and commercial applications remains underexplored. We present Sketch-Search Agent, a novel framework that transforms the image search experience by integrating a multimodal language agent with freehand sketches as control signals for diffusion models. Using the T2I-Adapter, Sketch-Search Agent combines sketches and text prompts to generate high-quality query images, encoded via a CLIP image encoder for efficient matching against an image corpus. Unlike existing methods, Sketch Search Agent requires minimal setup, no additional training, and excels in sketch-based image retrieval and natural language interactions. The multimodal agent enhances user experience by dynamically retaining preferences, ranking results, and refining queries for personalized recommendations. This interactive design empowers users to create sketches and receive tailored product suggestions, showcasing the potential of diffusion models in user-centric image retrieval. Experiments confirm Sketch-Search Agent's high accuracy in delivering relevant product search results
WasmGuard: Enhancing Web Security through Robust Raw-Binary Detection of WebAssembly Malware
Authors: Yuxia Sun, Huihong Chen, Zhixiao Fu, Wenjian Lv, Zitao Liu, Haolin Liu
Venue: WWW
Year: 2025

WebAssembly (Wasm), a binary instruction format designed for efficient cross-platform execution, has rapidly become a foundational web standard, widely adopted in browsers, client-side, and server-side applications. However, its growing popularity has led to an increase in Wasm-targeted malware, including cryptojackers and obfuscated malicious scripts, which pose significant threats to web security. In spite of progress in deep learning based detection methods for Wasm malware, such as MINOS, these approaches face substantial performance degradation in adversarial environments. In our experiments, MINOS's detection accuracy dropped to 49.90% under adversarial attacks, revealing critical vulnerabilities. To address this, we introduce WasmGuard, a robust malware detection framework tailored for Wasm. WasmGuard employs FGSM-based adversarial training with prior-based initialization for perturbation bytes in customized sections, coupled with a novel adversarial contrastive learning objective. Using our large-scale dataset, WasmMal-15K (publicly available at https://github.com/Yuxia-Sun/WasmMal GitHub), WasmGuard outperforms six competing methods, achieving up to 99.20% Robust Accuracy and 99.93% Standard Accuracy under PGD-50 adversarial attacks, while maintaining low training overhead. Additionally, we have released WebChecker, a WasmGuard-powered browser plugin, providing real-time protection against malicious Wasm files, at https://github.com/Yuxia-Sun/WasmGuard.
One for All: A Review of Large Pre-training Models for Re-Identification
Authors: Yubing Sun, Shuguang Dou, Ding Qi, Yanping Li, Jiale Zhao, Cairong Zhao
Venue: WWW
Year: 2025

Person re-identification (ReID) aims to match a target pedestrian across non-overlapping camera views, a task that faces significant challenges such as data dependency and overfitting due to the lim- ited availability of annotated datasets. Recently, large pre-training models, such as CLIP, have emerged as a transformative paradigm in visual learning. These models generate universal features with strong discriminative power and generalization capabilities, making them highly suitable for diverse ReID tasks. In this paper, we present a comprehensive survey of large pre-training models for ReID, which we refer to as the ''One for All'' approach. This paradigm leverages large-scale pre-training models, such as self-supervised or language- image pre-training models, as general feature extractors adaptable to multiple ReID tasks. We provide an in-depth analysis of the strengths of CLIP and DINO series, summarizing its application and advance- ments across six extensively studied ReID directions and we review self-supervised pre-training approaches for ReID and related tasks, highlighting their ability to achieve task-agnostic adaptability. Our findings demonstrate the immense potential of large pre-training models in advancing ReID research, offering valuable insights for future developments in this field. Relevant datasets and documents are at https:// github.com/ Vill-Lab/ Awesome-Evolving-ReID.
Advanced Hypergraph Mining for Web Applications Using Sphere Neural Networks
Authors: Zhongtian Sun, Anoushka Harit, Jongmin Yu, Jingyun Wang, Pietro Liò
Venue: WWW
Year: 2025

Web-based applications often involve analyzing complex multi-relational data generated by various domains, including social platforms, bibliographic networks, recommendation systems, and e-commerce platforms. Traditional graph-based methods struggle to model interactions beyond simple pairwise relationships, such as higher-order dependencies and the underlying geometric and structural properties of the data. This paper presents a novel application of hyperspherical deep learning to hypergraphs, integrating geometric hypergraph mining with a Sphere Neural Network (SNN) to model and analyze these intricate relationships effectively. Using real-world datasets, including Reddit, DBLP, MovieLens, and Amazon Co-purchase, our framework embeds hypergraphs into hyperspherical spaces, preserving both relational and geometric properties. Experimental results demonstrate that our method significantly improves performance on tasks such as recommendation, co-purchase prediction, and user behavior analysis, outperforming state-of-the-art techniques. This work highlights the potential of integrating geometric hypergraphs and hyperspherical deep learning to advance the analysis of web-based data.
Docs2KG: A Human-LLM Collaborative Approach to Unified Knowledge Graph Construction from Heterogeneous Documents
Authors: Qiang Sun, Yuanyi Luo, Wenxiao Zhang, Sirui Li, Jichunyang Li, Kai Niu, Xiangrui Kong, Wei Liu
Venue: WWW
Year: 2025

Enterprises generate vast amounts of unstructured documents, posing challenges for knowledge extraction and representation. Large language models (LLMs) offer strong potential for processing such data but struggle with factual accuracy and provenance. Knowledge graphs (KGs) provide a structured framework to address these limitations [6], yet constructing high-quality KGs from heterogeneous data remains a challenge. To address this issue, we present Docs2KG, a modular framework to build high-quality KGs from diverse unstructured documents. We first employs state-of-the-art document processing techniques to extract textual content, tabular data, and figures. The extracted information is then unified into a multifaceted KG with three aspects: (1) a Layout KG capturing document structural hierarchies, (2) a Metadata KG preserving document properties, and (3) a Semantic KG representing domain-specific entities and relationships. Docs2KG supports multiple construction paradigms for Semantic KG: ontology-based approaches, hybrid NLP pipelines with LLM verification, LLM-guided ontology generation, and specialized models for named entity recognition, event extraction, and causal relationship identification to enhance semantic coverage and accuracy. A key feature of Docs2KG is its human-in-the-loop verification interface, enabling iterative quality assessment and refinement of the resulting KGs. Docs2KG is openly available at https://docs2kg.ai4wa.com, with the aim of advancing knowledge graph construction research and accelerating enterprise applications through high-quality knowledge graph construction.
Effectiveness of Privacy-preserving Algorithms in LLMs: A Benchmark and Empirical Analysis
Authors: Jinglin Sun, Basem Suleiman, Imdad Ullah, Imran Razzak
Venue: WWW
Year: 2025

Preserving individual privacy is crucial when interacting with Large Language Models (LLMs) during both training and inference stages. Privacy leakage at either stage can lead to irreversible negative consequences. Although data-level privacy-preserving algorithms have been developed for smaller Natural Language Processing (NLP) models, their application to LLMs has not been extensively explored. Moreover, with plenty of algorithms emerging, it brings challenges for organizations or researchers to compare and evaluate these different algorithms to select the most suitable one for their certain requirements. To address these challenges, we introduce ''Privacy-preserving4LLM Benchmarking'', a systematic evaluation framework that systematically assesses different privacy-preserving algorithms' utility-privacy trade-offs across different LLM architectures. Our framework evaluates these algorithms in three practical scenarios: protecting training data only, user queries only, and both. We also introduce a novel Parameter Optimizer to ensure fair comparisons. To quantify privacy protection levels, we use exposure metrics, where canary data sequences are intentionally inserted into training data to measure information memorization and potential leakage. Our study presents a comprehensive empirical analysis comparing three privacy-preserving algorithms across three LLM architectures (Mistral-7B, Llama2-7b, Falcon-7b) using three different datasets. Our findings reveal that algorithm selection, protection scenarios, LLM architectures, and privacy budget settings all impact the utility and privacy level.
Scalable Algorithms for Forest-Based Centrality on Large Graphs
Authors: Yubo Sun, Haoxin Sun, Zhongzhi Zhang
Venue: WWW
Year: 2025

Centrality measures are essential for identifying important nodes and edges in networks. In this paper, we focus on two forest-based centrality measures on undirected graphs: forest node centrality (FNC) and forest edge centrality (FEC), which capture the influence of nodes and edges through their participation in spanning forests. Both centrality measures can be represented using entries of the forest matrix. To address the challenge of computing the two measures on large networks, we propose two scalable algorithms from different perspectives. The first algorithm IFGN combines two variance reduction techniques to approximate the entries of the forest matrix, applicable to both FNC and FEC.The second algorithm FECE incorporates a new physical interpretation of FEC, allowing for a better overall estimation. We provide error guarantees for both algorithms and demonstrate their efficiency and effectiveness through extensive experiments on various real-world networks.
AdvTG: An Adversarial Traffic Generation Framework to Deceive DL-Based Malicious Traffic Detection Models
Authors: Peishuai Sun, Xiaochun Yun, Shuhao Li, Tao Yin, Chengxiang Si, Jiang Xie
Venue: WWW
Year: 2025

Deep learning-based (DL-based) malicious traffic detection models are effective but vulnerable to adversarial attacks. Existing adversarial attacks have shown promising results when targeting traffic detection models based on statistics and sequence features. However, these attacks are less effective against models that rely on payload analysis. The main reason is the difficulty in generating semantic, compliant, and functional payloads, which limits their practical application. In this paper, we propose AdvTG, an adversarial traffic generation framework to deceive DL-based malicious traffic based on the large language model (LLM) and reinforcement learning (RL). Specifically, AdvTG is designed to attack various DL-based detection models across diverse payload features and architectures, thereby enhancing the generalization capabilities of the generated adversarial traffic. Moreover, we design a specialized prompt for traffic generation tasks, where functional fields and target types are supplied as input, while non-functional fields are generated to produce the mutated traffic. This fine-tuning endows the LLM with task comprehension and traffic pattern reasoning abilities, allowing it to generate traffic that remains compliant and functional. Furthermore, leveraging RL, AdvTG automatically selects traffic fields that exhibit more robust adversarial properties. Experimental results show that AdvTG achieves over 40% attack success rate (ASR) across six detection models on four base datasets and two extended datasets, significantly outperforming other adversarial attack methods.
Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment
Authors: Kristen Sussman, Daniel Carter
Venue: WWW
Year: 2025

Given the subtle human-like effects of large language models on linguistic patterns, this study examines shifts in language over time to detect the impact of AI-mediated communication (AI-MC) on social media. We compare a replicated dataset of 970,919 tweets from 2020 (pre-ChatGPT) with 20,000 tweets from the same period in 2024, all of which mention Donald Trump during election periods. Using a combination of Flesch-Kincaid readability and polarity scores, we analyze changes in text complexity and sentiment. Our findings reveal a significant increase in mean sentiment polarity (0.12 vs. 0.04) and a shift from predominantly neutral content (54.8% in 2020 to 39.8% in 2024) to more positive expressions (28.6% to 45.9%). These findings suggest not only an increasing presence of AI in social media communication but also its impact on language and emotional expression patterns.
EdgePrompt: Engineering Guardrail Techniques for Offline LLMs in K-12 Educational Settings
Authors: Riza Alaudin Syah, Christoforus Yoga Haryanto, Emily Lomempow, Krishna Malik, Irvan Putra
Venue: WWW
Year: 2025

EdgePrompt is a prompt engineering framework that implements pragmatic guardrails for Large Language Models (LLMs) in the K-12 educational settings through structured prompting inspired by neural-symbolic principles. The system addresses educational disparities in Indonesia's Frontier, Outermost, Underdeveloped (3T) regions by enabling offline-capable content safety controls. It combines: (1) content generation with structured constraint templates, (2) assessment processing with layered validation, and (3) lightweight storage for content and result management. The framework implements a multi-stage verification workflow that maintains safety boundaries while preserving model capabilities in connectivity-constrained environments. Initial deployment targets Grade 5 language instruction, demonstrating effective guardrails through structured prompt engineering without formal symbolic reasoning components.
Bridge-Generate: Scholarly Hybrid Question Answering
Authors: Tilahun Abedissa Taffa, Ricardo Usbeck
Venue: WWW
Year: 2025

Answering scholarly hybrid questions requires access to bibliographic facts stored in structured data, such as a Knowledge Graph (KG) and textual information. Existing Scholarly Hybrid Question Answering (SHQA) approaches rely on retrieving KG triples and documents from the Wikipedia text corpus and prompt an LLM (Large Language Model) for answers. However, the retrieval is heavily keyword-based, introducing noise into the context. Furthermore, despite detecting the entities in the question, the models do not attempt any question analysis. Therefore, we propose a new SHQA system that employs a bridge-generate approach. During the bridge phase, our system recursively identifies entity-encapsulating phrases within the question and resolves the entities leveraging the underlying KGs. It then formulates assertion statements based on the resolved entities and their corresponding phrases. In the generation phase, the system auto-generates context guided by the question and the assertions. Finally, it returns an answer prompting an LLM with the generated context, the assertions, and the question. Our approach outperforms previous approaches, addressing the identified gaps.