OpenRaiser/Intern-Atlas huggingface dataset:
Hyperbolic Diffusion Recommender Model
Authors: Meng Yuan, Yutian Xiao, Wei Chen, Chou Zhao, Deqing Wang, Fuzhen Zhuang
Venue: WWW
Year: 2025

Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender systems, we investigate the fundamental structural disparities between images and items. Consequently, items often exhibit distinct anisotropic and directional structures that are less prevalent in images. However, the traditional forward diffusion process continuously adds isotropic Gaussian noise, causing anisotropic signals to degrade into noise, which impairs the semantically meaningful representations in recommender systems. Inspired by the advancements in hyperbolic spaces, we propose a novel Hyperbolic Diffusion Recommender Model (named HDRM). Unlike existing directional diffusion methods based on Euclidean space, the intrinsic non-Euclidean structure of hyperbolic space makes it particularly well-adapted for handling anisotropic diffusion processes. In particular, we begin by constructing a geometrically latent space grounded in hyperbolic geometry, incorporating interpretability measures to define the latent anisotropic diffusion processes. Subsequently, we propose a novel hyperbolic latent diffusion process specifically tailored for users and items. Drawing upon the natural geometric attributes of hyperbolic spaces, we restrict both radial and angular components to facilitate directional diffusion propagation, thereby ensuring the preservation of the original topological structure in user-item interaction graphs. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HDRM. Our code is available at https://github.com/yuanmeng-cpu/HDRM.
Heterogeneous Temporal Graph Neural Networks for Link Prediction
Authors: Chongjian Yue, Lun Du, Mengyuan Chen
Venue: WWW
Year: 2025

Link prediction stands as a critical research frontier in graph data analytics. In real-world scenarios, graph data commonly manifest heterogeneity and temporal dynamism, thereby adding layers of complexity to analytical models. Existing work either considers heterogeneity or temporal dynamism, with very little work modeling both properties simultaneously. In this study, we propose a novel link prediction framework that is specifically designed to integrate heterogeneity and temporal variations within graph data, which can be used in a wide range of domains. In the framework, we design a Heterogeneous Temporal Graph Neural Network (HTGN) model and a new training strategy, named Bi-Window Strategy(BWS). The HTGN is capable of synthesizing temporal node representations by aggregating heterogeneous temporal information. Simultaneously, the Bi-Window Strategy (BWS) enhances the model's ability to capture the long-term distributional characteristics inherent in graph data. Our approach achieved 2nd-Winner Award of Temporal Link Prediction task at WSDM'22 Cup. In this paper, we incorporate additional experiments using a more equitable data-splitting approach and perform comparisons with an expanded range of baseline methods.
Empowering Federated Graph Rationale Learning with Latent Environments
Authors: Linan Yue, Qi Liu, Yawen Li, Fangzhou Yao, Weibo Gao, Junping Du
Venue: WWW
Year: 2025

The success of Graph Neural Networks (GNNs) in graph classification has heightened interest in explainable GNNs, particularly through graph rationalization. This method aims to enhance GNNs explainability by identifying subgraph structures (i.e., rationales) that support model predictions. However, existing methods often rely on centralized datasets, posing challenges in scenarios where data privacy is crucial, such as in molecular property prediction. Federated Learning (FL) offers a solution by enabling collaborative model training without sharing raw data. In this context, Federated Graph Rationalization emerges as a promising research direction. However, in each client, the rationalization methods often rely on client-specific shortcuts to compose rationales and make task predictions. Data heterogeneity, characterized by non-IID data across clients, exacerbates this problem, leading to poor prediction performance. To address these challenges, we propose the Environment-aware Data Augmentation (EaDA) method for Federated Graph Rationalization. EaDA comprises two main components: the Environment-aware Rationale Extraction (ERE) module and the Local-Global Alignment (LGA) module. The ERE module employs prototype learning to infer and share abstract environment information across clients, which are then aggregated to form a global environment. This information is used to generate counterfactual samples for local clients, enhancing the robustness of task predictions. The LGA module uses contrastive learning methods to align local and global rationale representations, mitigating performance degradation due to data heterogeneity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of our approaches. Code is available at https://github.com/yuelinan/Codes-of-EaDA.
Graph Wave Networks
Authors: Juwei Yue, Haikuo Li, Jiawei Sheng, Yihan Guo, Xinghua Zhang, Chuan Zhou, Tingwen Liu, Li Guo
Venue: WWW
Year: 2025

Dynamics modeling has been introduced as a novel paradigm in message passing (MP) of graph neural networks (GNNs). Existing methods consider MP between nodes as a heat diffusion process, and leverage heat equation to model the temporal evolution of nodes in the embedding space. However, heat equation can hardly depict the wave nature of graph signals in graph signal processing. Besides, heat equation is essentially a partial differential equation (PDE) involving a first partial derivative of time, whose numerical solution usually has low stability, and leads to inefficient model training. In this paper, we would like to depict more wave details in MP, since graph signals are essentially wave signals that can be seen as a superposition of a series of waves in the form of eigenvector. This motivates us to consider MP as a wave propagation process to capture the temporal evolution of wave signals in the space. Based on wave equation in physics, we innovatively develop a graph wave equation to leverage the wave propagation on graphs. In details, we demonstrate that the graph wave equation can be connected to traditional spectral GNNs, facilitating the design of graph wave networks based on various Laplacians and enhancing the performance of the spectral GNNs. Besides, the graph wave equation is particularly a PDE involving a second partial derivative of time, which has stronger stability on graphs than the heat equation that involves a first partial derivative of time. Additionally, we theoretically prove that the numerical solution derived from the graph wave equation are constantly stable, enabling to significantly enhance model efficiency while ensuring its performance. Extensive experiments show that GWNs achieve SOTA and efficient performance on benchmark datasets, and exhibit outstanding performance in addressing challenging graph problems, such as over-smoothing and heterophily.
Multi-Model Workflows for Advanced Visual Understanding
Authors: Arun George Zachariah, Varun Praveen, Samuel Ochoa, Parthasarathy Sriram
Venue: WWW
Year: 2025

This paper introduces a novel task-agnostic system for visual question answering, designed to address the limitations of existing visual-language models in scenarios requiring fine-grained reasoning and contextual understanding. By integrating computer vision, natural language processing, and retrieval-augmented generation, the system dynamically selects models and interprets diverse visual inputs without necessitating task-specific retraining. It leverages scene graph extraction and a model multiplexer to process visual content contextually, while an instruction execution engine transforms multimodal data into actionable insights. The system emphasizes explainability through annotated outputs and textual responses, enhancing user trust and comprehension. Its modular design ensures scalability and adaptability, enabling seamless incorporation of new models and datasets for evolving applications. This work represents a significant step forward in visual question answering, offering a transparent and versatile solution tailored to real-world complexities. The complete source code can be found at https://github.com/NVIDIA/Multi-Model-Workflows.
Assessing Compliance in Digital Advertising: A Deep Dive into Acceptable Ads Standards
Authors: Ahsan Zafar, Anupam Das
Venue: WWW
Year: 2025

Online ads provide essential revenue for millions of websites but often disrupt user experience. To address this, browser extensions emerged to block intrusive ads, prompting the creation of the Acceptable Ads Standards to balance user choice and monetization. The Acceptable Ads Standards, initiated by the Acceptable Ads Committee, seek a balance between user experience and ad effectiveness, allowing certain non-intrusive ads defined by size, placement, and type limitations. This paper analyzes the compliance of digital advertisements with the Acceptable Ads standards by examining 10,000 popular domains intersecting Tranco's top 100K and the Acceptable Ads exception list. Our findings reveal that nearly 10% of these sites display non-compliant ads on landing pages, exposing design flaws in the exception list that allow publishers to bypass size and format restrictions. We propose enhancements to the exception list to better uphold user experience and ad integrity.
Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection
Authors: Long Zeng, Jianxiang Yu, Jiapeng Zhu, Qingsong Zhong, Xiang Li
Venue: WWW
Year: 2025

Graph self-supervised learning has gained significant attention recently. However, many existing approaches heavily depend on perturbations, and inappropriate perturbations may corrupt the graph's inherent information. The Vector Quantized Variational Autoencoder (VQ-VAE) is a powerful autoencoder extensively used in fields such as computer vision; however, its application to graph data remains underexplored. In this paper, we provide an empirical analysis of vector quantization in the context of graph autoencoders, demonstrating its significant enhancement of the model's capacity to capture graph topology. Furthermore, we identify two key challenges associated with vector quantization when applying in graph data: codebook underutilization and codebook space sparsity. For the first challenge, we propose an annealing-based encoding strategy that promotes broad code utilization in the early stages of training, gradually shifting focus toward the most effective codes as training progresses. For the second challenge, we introduce a hierarchical two-layer codebook that captures relationships between embeddings through clustering. The second layer codebook links similar codes, encouraging the model to learn closer embeddings for nodes with similar features and structural topology in the graph. Our proposed model outperforms 16 representative baseline methods in self-supervised link prediction and node classification tasks across multiple datasets. Our implementation is available at https://github.com/vitaminzl/hqa-gae.
LIRA: A Learning-based Query-aware Partition Framework for Large-scale ANN Search
Authors: Ximu Zeng, Liwei Deng, Penghao Chen, Xu Chen, Han Su, Kai Zheng
Venue: WWW
Year: 2025

Approximate nearest neighbor search is fundamental in information retrieval. Previous partition-based methods enhance search efficiency by probing partial partitions, yet they face two common issues. In the query phase, a common strategy is to probe partitions based on the distance ranks of a query to partition centroids, which inevitably probes irrelevant partitions as it ignores data distribution. In the partition construction phase, all partition-based methods face the boundary problem that separates a query's nearest neighbors to multiple partitions, resulting in a long-tailed kNN distribution and degrading the optimal nprobe (i.e., the number of probing partitions). To address this gap, we propose LIRA, a LearnIng-based queRy-aware pArtition framework. Specifically, we propose a probing model to directly probe the partitions containing the kNN of a query, which can reduce probing waste and allow for query-aware probing with nprobe individually. Moreover, we incorporate the probing model into a learning-based redundancy strategy to mitigate the adverse impact of the long-tailed kNN distribution on search efficiency. Extensive experiments on real-world vector datasets demonstrate the superiority of LIRA in the trade-off among accuracy, latency, and query fan-out. The codes are available at https://github.com/SimoneZeng/LIRA-ANN-search.
Understanding the Design Principles of Link Prediction in Directed Settings
Authors: Jun Zhai, Muberra Ozmen, Thomas Markovich
Venue: WWW
Year: 2025

Link prediction is a widely studied task in Graph Representation Learning (GRL) for modeling relational data. The early theories in GRL were based on the assumption of a symmetric adjacency matrix, reflecting an undirected setting. As a result, much of the following state-of-the-art research has continued to operate under this symmetry assumption, even though real-world data often involve crucial information conveyed through the direction of relationships. This oversight limits the ability of these models to fully capture the complexity of directed interactions. In this paper, we focus on the challenge of directed link prediction by evaluating key heuristics that have been successful in undirected settings. We propose simple but effective adaptations of these heuristics to the directed link prediction task and demonstrate that these modifications produce competitive performance compared to the leading Graph Neural Networks (GNNs) originally designed for undirected graphs. Through an extensive set of experiments, we derive insights that inform the development of a novel framework for directed link prediction, which not only surpasses baseline methods but also outperforms state-of-the-art GNNs on multiple benchmarks.
Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator
Authors: Yusong Zhang, Kun Xie, Xingyi Zhang, Xiangyu Dong, Sibo Wang
Venue: WWW
Year: 2025

The spread of rumors on social media, particularly during significant events like the US elections and the COVID-19 pandemic, poses a serious threat to social stability and public health. Current rumor detection methods primarily rely on propagation graphs to improve the model performance. However, the effectiveness of these methods is often compromised by noisy and irrelevant structures in the propagation process. To tackle this issue, techniques such as weight adjustment and data augmentation have been proposed. However, they depend heavily on rich original propagation structures, limiting their effectiveness in handling rumors that lack sufficient propagation information, especially in the early stages of dissemination. In this work, we introduce the Key Propagation Graph Generator (KPG), a novel reinforcement learning-based framework, that generates contextually coherent and informative propagation patterns for events with insufficient topology information and identifies significant substructures in events with redundant and noisy propagation structures. KPG comprises two key components: the Candidate Response Generator (CRG) and the Ending Node Selector (ENS). CRG learns latent variable distributions from refined propagation patterns to eliminate noise and generate new candidates for ENS, while ENS identifies the most influential substructures in propagation graphs and provides training data for CRG. Furthermore, we develop an end-to-end framework that utilizes rewards derived from a pre-trained graph neural network to guide the training process. The resulting key propagation graphs are then employed in downstream rumor detection tasks. Extensive experiments conducted on four datasets demonstrate that KPG outperforms current state-of-the-art methods.
MER-Inspector: Assessing Model Extraction Risks from An Attack-Agnostic Perspective
Authors: Xinwei Zhang, Haibo Hu, Qingqing Ye, Li Bai, Huadi Zheng
Venue: WWW
Year: 2025

Information leakage issues in machine learning-based Web applications have attracted increasing attention. While the risk of data privacy leakage has been rigorously analyzed, the theory of model function leakage, known as Model Extraction Attacks (MEAs), has not been well studied. In this paper, we are the first to understand MEAs theoretically from an attack-agnostic perspective and to propose analytical metrics for evaluating model extraction risks. By using the Neural Tangent Kernel (NTK) theory, we formulate the linearized MEA as a regularized kernel classification problem and then derive the fidelity gap and generalization error bounds of the attack performance. Based on these theoretical analyses, we propose a new theoretical metric called Model Recovery Complexity (MRC), which measures the distance of weight changes between the victim and surrogate models to quantify risk. Additionally, we find that victim model accuracy, which shows a strong positive correlation with model extraction risk, can serve as an empirical metric. By integrating these two metrics, we propose a framework, namely Model Extraction Risk Inspector (MER-Inspector), to compare the extraction risks of models under different model architectures by utilizing relative metric values. We conduct extensive experiments on 16 model architectures and 5 datasets. The experimental results demonstrate that the proposed metrics have a high correlation with model extraction risks, and MER-Inspector can accurately compare the extraction risks of any two models with up to 89.58%.
The 3rd Workshop on Personal Intelligence with Generative AI
Authors: Yang Zhang, Wenjie Wang, Xinyu Lin, Fuli Feng, Hongzhi Yin, Wayne Xin Zhao, Lina Yao, Yang Song, Xiangnan He
Venue: WWW
Year: 2025

The emergence of large language models (LLMs) has ushered in a new era of generative AI, exhibiting broad human-level capabilities and achieving meaningful progress toward Artificial General Intelligence (AGI). To realize AGI that truly benefits each individual, one next key direction for generative AI is to develop personal intelligence -understanding individuals and enhancing personal thinking, planning, and life experiences through personalization. Although evolving generative AI toward personal intelligence is emerging, it has attracted considerable interest from both research institutions and industry, even becoming a strategic direction for many companies. This workshop aims to provide a platform for discussing innovative ideas that facilitate personal intelligence with generative AI, 1) envisioning the future of personal intelligence powered by generative AI, 2) item establishing benchmarks for personal intelligence tasks, 3) advancing user modeling/personalization techniques for generative models/agents, 4) enhancing real-world applications, and 4) ensuring privacy and trustworthiness. This workshop aims to deepen understanding, accelerate progress, and support the transformative development of personal intelligence. The website for the workshop is: https://generative-rec.github.io/workshop-www25.
Web-Centric Human Mobility Analytics: Methods, Applications, and Future Directions in the LLM Era
Authors: Zijian Zhang, Hao Miao, Yuxuan Liang, Yan Zhao, Xiao Han, Pengyue Jia, Bin Yang, Christian S. Jensen
Venue: WWW
Year: 2025

Human mobility analytics is essential to enabling a broad range of web-related applications, such as navigation, urban planning, and point-of-interest (POI) recommendation. The proliferation of mobility data, including geo-social media check-ins and geo-location data, offers unprecedented opportunities for analyzing human mobility. This lecture-style tutorial offers an in-depth look at web-centric human mobility analytics, organized according to three levels: location-level, individual-level, and macro-level. Location-level analytics focus on spatial activities within specific geographical locations, using points of interest and other data to forecast future visits and identify urban mobility patterns. Individual-level analytics delve into the movements of individuals, e.g., considering sequences of visited locations over time, elucidating individual movement behaviors. Macro-level analytics broaden the scope of analyses to include large-scale spatial patterns and population flows across regions, offering a macro perspective on mobility. The tutorial encompasses cutting-edge learning frameworks such as federated learning as well as continual learning and innovative applications of Large Language Models (LLMs), which enhance predictive analytics and expand the capabilities of mobility analysis. The tutorial aims to afford the participants a comprehensive overview of the current state and future directions of web-centric human mobility analytics, making it an invaluable resource for using web-sourced human mobility data to facilitate a more informed and interconnected world. The video teaser is available at https://shorturl.at/HShNc.
Tackling Sparse Facts for Temporal Knowledge Graph Completion
Authors: Yuchao Zhang, Xiangjie Kong, Kailun Ye, Guojiang Shen, Shangfei Zheng
Venue: WWW
Year: 2025

Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in downstream tasks. Nonetheless, real-world knowledge-such as the progression of social network interactions and the unfolding of news events-is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. ANEL first derives initial entity embeddings through a base model and then uncovers concealed semantic relationships between entities via a latent relation module, enriching the explicit relationships within the knowledge graph. Furthermore, ANEL incorporates an adaptive latent information adjustment component, which dynamically calibrates the influence of latent information based on the entity's relational structure: entities with fewer connections derive greater benefit from latent information, while entities with denser connections become less dependent on latent augmentation, ensuring precise and resilient representations. We conducted comprehensive experiments on four prominent benchmark datasets, and the results underscore the effectiveness and superiority of ANEL in TKGC tasks.
Enhancing Text-Based Person Retrieval via Loss Balancing and Vision-Language Models
Authors: Jizheng Zhang
Venue: WWW
Year: 2025

This paper presents the ZhCheng team's solution to the WWW 2025 challenge on Text-based Person Anomaly Search (TPAS). Text-based person retrieval aims to retrieve specific individuals using natural language descriptions. A key challenge in this field is recognizing anomalous behaviors in real-world scenarios, such as unusual actions, rather than common ones like walking or standing. Existing state-of-the-art methods underperform on this new task. After experiments, we identified two aspects-loss functions and architecture-that can significantly enhance performance. First, we introduce a strategy that leverages homoscedastic uncertainty to balance multiple loss functions. Second, considering the remarkable success of pretrained vision-language models (VLMs) in vision language tasks, we adopt a pretrained VLM as the backbone and fine-tune it on the existing benchmark. Experimental results demonstrate that our proposed approach effectively improves the performance of current baselines on this challenging task.