OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
Authors: Zhe Wang, Sheng Zhou, Jiawei Chen, Zhen Zhang, Binbin Hu, Yan Feng, Chun Chen, Can Wang
Venue: WSDM
Year: 2025

Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement for representation learning in CTDGs is the appropriate estimation and preservation of proximity. However, due to the sparse and evolving characteristics of CTDGs, the spatial-temporal properties inherent in high-order proximity remain largely unexplored. Despite its importance, this property presents significant challenges due to the computationally intensive nature of personalized interaction intensity estimation and the dynamic attributes of CTDGs. To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process. Building on this, we introduce the Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains the evolving spatial-temporal high-order proximity for effective node representation learning in CTDGs. Extensive experiments on seven small and two large-scale datasets demonstrate the superior performance and scalability of the proposed CorDGT. The code is available at: https://github.com/wangz3066/CorDGT.
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
Authors: Botao Wang, Jia Li, Heng Chang, Keli Zhang, Fugee Tsung
Venue: WSDM
Year: 2025

In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect analysis, we can discern heterophilic edges based on asymmetric node dependency. The learned causal structure offers more accurate relationships among nodes. To reduce the computational complexity, we introduce intervention-based causal inference in graph learning. We first simplify causal analysis on graphs by formulating it as a structural learning model and define the optimization problem within the Bayesian scheme. We then present an analysis of decomposing the optimization target into a consistency penalty and a structure modification based on cause-effect relations. We then estimate this target by conditional entropy and present insights into how conditional entropy quantifies the heterophily. Accordingly, we propose CausalMP, a causal message-passing discovery network for heterophilic graph learning, that iteratively learns the explicit causal structure of input graphs. We conduct extensive experiments in both heterophilic and homophilic graph settings. The result demonstrates that the our model achieves superior link prediction performance. Training on causal structure can also enhance node representation in classification task across different base models.
Exploration and Exploitation of Hard Negative Samples for Cross-Domain Sequential Recommendation
Authors: Yidan Wang, Xuri Ge, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Zhumin Chen, Jun Ma, Xin Xin
Venue: WSDM
Year: 2025

Negative sampling plays a crucial role for cross-domain recommendation as it provides contrastive signals to learn user preference. Existing methods usually select items with high predicted scores or popularity as hard negative samples to improve model training. However, such methods suffer from choosing false negative samples since items with high predicted scores or popularity could also indicate potential positive user preference. Although several studies devoted to discovering true negative samples, few of them leverage user cross-domain behaviors to alleviate the false negative issue. How to effectively mine and utilize hard negative samples to improve cross-domain recommendation remains an open question. In this work, we propose exploration and exploitation of hard negative samples (EXHANS) for cross-domain sequential recommendation. For better exploration, we utilize the user preference from the source domain to guide negative sampling in the target domain. The key idea is that compared with hard negative samples, false negative samples have higher probability to be consistent with the user preference in both domains. Besides, we propose adaptive popularity-based score correction to account for users' different tastes of popular items. The idea is that for users who favor popular items, such items are more likely to be false negatives rather than hard negatives. For better exploitation, we design a replay buffer to cache the obtained negative samples and further propose a curriculum learning framework to balance exploration and exploitation of hard negative samples. Extensive experiments on three real-world datasets show that our method significantly outperforms state-of-the-art negative sampling methods for cross-domain sequential recommendation, which verify the effectiveness of EXHANS.
Your Causal Self-Attentive Recommender Hosts a Lonely Neighborhood
Authors: Yueqi Wang, Zhankui He, Zhenrui Yue, Julian J. McAuley, Dong Wang
Venue: WSDM
Year: 2025

In the context of sequential recommendation, a pivotal issue pertains to the comparative analysis between bi-directional/auto-encoding (AE) and uni-directional/auto-regressive (AR) attention mechanisms, where the conclusions regarding architectural and performance superiority remain inconclusive. Previous efforts in such comparisons primarily involve summarizing existing works to identify a consensus or conducting ablation studies on peripheral modeling techniques, such as choices of loss functions. However, far fewer efforts have been made in (1) theoretical and (2) extensive empirical analysis of the self-attention module, the very pivotal structure on which performance and designing insights should be anchored. In this work, we first provide a comprehensive theoretical analysis of AE/AR attention matrix in the aspect of (1) sparse local inductive bias, a.k.a neighborhood effects, and (2) low rank approximation. Analytical metrics reveal that the AR attention exhibits sparse neighborhood effects suitable for generally sparse recommendation scenarios. Secondly, to support our theoretical analysis, we conduct extensive empirical experiments on comparing AE/AR attention on five popular benchmarks with AR performing better overall. Empirical results reported are based on our experimental pipeline named Modularized Design Space for Self-Attentive Recommender (ModSAR), supporting adaptive hyperparameter tuning, modularized design space and HuggingFace plug-ins. We invite the recommendation community to utilize/contribute to ModSAR to (1) conduct more module/model-level examining beyond AE/AR comparison and (2) accelerate state-of-the-art model design. Lastly, we shed light on future design choices for performant self-attentive recommenders. We make our pipeline implementation and data available at https://github.com/yueqirex/SAR-Check.
Adjacent Neighborhood Transformer-based Diffusion Model for Anomaly Detection under Incomplete Industrial Data Sources
Authors: Lulu Wang, Chengqing Li
Venue: WSDM
Year: 2025

Anomaly detection in an industrial setting is crucial for operational monitoring, yet it remains challenging under incomplete data conditions. Common issues, such as sensor failures, data transmission loss, and storage malfunctions, often result in missing data, complicating the detection of anomalies, particularly when these are localized within specific regions. To address the challenges, this paper proposes DiffANT, an unsupervised anomaly detection method that integrates a diffusion model with the Adjacent Neighborhood Transformer (ANT). Specifically, DiffANT begins by applying various data masking techniques to simulate realistic missing values that reflect real-world industrial scenarios. The ANT utilizes a Transformer encoder architecture augmented with an adjacent neighborhood attention mechanism. It effectively focuses on relevant non-immediate vicinities to enhance anomaly detection. DiffANT then reconstructs the original data from randomly sampled noise through diffusion and denoising processes and utilizes a multi-level reconstruction strategy to refine the generated samples. We demonstrate the efficacy of DiffANT through extensive experiments in diverse industrial applications, such as secure water treatment and server machine monitoring. The results indicate that DiffANT consistently outperforms state-of-the-art methods in detecting anomalies in time series data, regardless of whether the data is incomplete.
Mining Topics towards ChatGPT Using a Disentangled Contextualized-neural Topic Model
Authors: Rui Wang, Xing Liu, Yanan Wang, Shuyu Chang, Yuanzhi Yao, Haiping Huang
Venue: WSDM
Year: 2025

Mining topics relevant to the advanced AI dialogue system, such as ChatGPT, from short-length posts on social media poses several challenges for existing topic-mining approaches. Firstly, Bag-Of-Words approaches, including probabilistic topic models and their embedding-based variants, may struggle to extract interpretable topics due to insufficient word co-occurrence. Secondly, contextualized based approaches, built on the autoencoding framework, often yield entangled topic spaces, resulting in the mixing of irrelevant words into topics. To address these limitations, we propose a novel Dis entangled Contextualized-neural Topic Model (DisCTM) based on textual representation learning. DisCTM leverages a pre-trained transformer language model to incorporate word sequence information and deal with the sparsity in short text. Additionally, it employs a topic disentangling mechanism to decorrelate dimensions of the latent topic space, effectively separating semantically irrelevant words into different topics. Extensive experiments have been conducted on three publicly available text corpora, and the results demonstrate the effectiveness of DisCTM in extracting high-quality topics, as measured by topic coherence and diversity metrics.
Sequential Diversification with Provable Guarantees
Authors: Honglian Wang, Sijing Tu, Aristides Gionis
Venue: WSDM
Year: 2025

Diversification is a useful tool for exploring large collections of information items. It has been used to reduce redundancy and cover multiple perspectives in information-search settings. Diversification finds applications in many different domains, including presenting search results of information-retrieval systems and selecting suggestions for recommender systems. Interestingly, existing measures of diversity are defined over sets of items, rather than evaluating sequences of items. This design choice comes in contrast with commonly-used relevance measures, which are distinctly defined over sequences of items, taking into account the ranking of items. The importance of employing sequential measures is that information items are almost always presented in a sequential manner, and during their information-exploration activity users tend to prioritize items with higher ranking. In this paper, we study the problem of maximizing sequential diversity. This is a new measure of diversity, which accounts for the ranking of the items, and incorporates item relevance and user behavior. The overarching framework can be instantiated with different diversity measures, and here we consider the measures of sum diversity and coverage diversity. The problem was recently proposed by Coppolillo et al. [11], where they introduce empirical methods that work well in practice. Our paper is a theoretical treatment of the problem: we establish the problem hardness and present algorithms with constant approximation guarantees for both diversity measures we consider. Experimentally, we demonstrate that our methods are competitive against strong baselines.
WildlifeLookup: A Chatbot Facilitating Wildlife Management with Accessible Data and Insights
Authors: Xiangqi Wang, Tianyu Yang, Jason R. Rohr, Brett Scheffers, Nitesh V. Chawla, Xiangliang Zhang
Venue: WSDM
Year: 2025

Wildlife management is increasingly reliant on data-driven insights to address the impacts of climate change on species and ecosystems. However, the complexity of accessing and querying large, multimodal datasets often limits the ability of non-technical users, such as wildlife managers and conservationists, to make informed decisions. To address this challenge, we present WildlifeLookup, a public accessible, intelligent chatbot designed to facilitate natural language interaction with a novel knowledge graph (KN-Wildlife) that houses critical wildlife and environmental data. WildlifeLookup simplifies access to species distributions, habitat interactions, and climate-related events by converting user queries into precise graph queries, reducing the technical barriers for end users. The chatbot WildlifeLookup is available at https://oknbot.ngrok.dev/
Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction
Authors: Qinyuan Wu, Mohammad Aflah Khan, Soumi Das, Vedant Nanda, Bishwamittra Ghosh, Camila Kolling, Till Speicher, Laurent Bindschaedler, Krishna P. Gummadi, Evimaria Terzi
Venue: WSDM
Year: 2025

In this paper, we focus on the challenging task of reliably estimating factual knowledge that is embedded inside large language models (LLMs). To avoid reliability concerns with prior approaches, we propose to eliminate prompt engineering when probing LLMs for factual knowledge. Our approach, called Zero-Prompt Latent Knowledge Estimator (ZP-LKE), leverages the in-context learning ability of LLMs to communicate both the factual knowledge question as well as the expected answer format. Our knowledge estimator is both conceptually simpler (i.e., doesn't depend on meta-linguistic judgments of LLMs) and easier to apply (i.e., is not LLM-specific), and we demonstrate that it can surface more of the latent knowledge embedded in LLMs. We also investigate how different design choices affect the performance of ZP-LKE. Using the proposed estimator, we perform a large-scale evaluation of the factual knowledge of a variety of open-source LLMs, like OPT, Pythia, Llama(2), Mistral, Gemma, etc. over a large set of relations and facts from the Wikidata knowledge base. We observe differences in the factual knowledge between different model families and models of different sizes, that some relations are consistently better known than others but that models differ in the precise facts they know, and differences in the knowledge of base models and their finetuned counterparts. Code available at: https://github.com/QinyuanWu0710/ZeroPrompt_LKE
Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation
Authors: Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu
Venue: WSDM
Year: 2025

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the ''oracle'' user preferences in the future and will be beneficial to model dynamic user preferences. Therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn ''forward-looking'' models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin.
MixRec: Heterogeneous Graph Collaborative Filtering
Authors: Lianghao Xia, Meiyan Xie, Yong Xu, Chao Huang
Venue: WSDM
Year: 2025

For modern recommender systems, the use of low-dimensional latent representations to embed users and items based on their observed interactions has become commonplace. However, many existing recommendation models are primarily designed for coarse-grained and homogeneous interactions, which limits their effectiveness in two critical dimensions. Firstly, these models fail to leverage the relational dependencies that exist across different types of user behaviors, such as page views, collects, comments, and purchases. Secondly, they struggle to capture the fine-grained latent factors that drive user interaction patterns. To address these limitations, we present a heterogeneous graph collaborative filtering model MixRec that excels at disentangling users' multi-behavior interaction patterns and uncovering the latent intent factors behind each behavior. Our model achieves this by incorporating intent disentanglement and multi-behavior modeling, facilitated by a parameterized heterogeneous hypergraph architecture. Furthermore, we introduce a novel contrastive learning paradigm that adaptively explores the advantages of self-supervised data augmentation, thereby enhancing the model's resilience against data sparsity and expressiveness with relation heterogeneity. To validate the efficacy of MixRec, we conducted extensive experiments on three public datasets. The results clearly demonstrate its superior performance, significantly outperforming various state-of-the-art baselines. Our model is open-sourced and available at: https://github.com/HKUDS/MixRec.
D2: Customizing Two-Stage Graph Neural Networks for Early Rumor Detection through Cascade Diffusion Prediction
Authors: Haowei Xu, Chao Gao, Xianghua Li, Zhen Wang
Venue: WSDM
Year: 2025

Early rumor detection is crucial for mitigating the widespread dissemination of misinformation. Existing methods predominantly rely on complete rumor diffusion graphs, which are challenging to obtain in real-world scenarios, complicating early detection efforts. To address this challenge, we propose D2, a two-stage framework for early rumor Detection, integrating cascade Diffusion prediction. This framework aims to enhance early rumor detection by incorporating diffusion prediction capabilities. Specifically, a dynamic heterogeneous graph neural network (GNN) is developed to jointly model users' social and propagation graphs, enabling accurate prediction of potential diffusion paths using limited observed data within short time windows. The inferred diffusion paths are then integrated with early-stage data, and GNNs are employed for graph classification. However, the varying data distributions across different social media platforms necessitate extensive tuning to optimize GNN architectures. To facilitate the detection of rumor diffusion graphs at the initial stages, a search space is designed across four dimensions- aggregation, merge, readout, and sequence functions-encompassing various GNN architectures. Subsequently, D2 employs an efficient differentiable search algorithm to identify high-performance GNNs within this search space. Experimental results on real social media datasets demonstrate that this approach significantly improves both the accuracy and robustness of early rumor detection.
DTPN: A Diffusion-based Traffic Purification Network for Tor Website Fingerprinting
Authors: Chenchen Yang, Xi Xiao, Guangwu Hu, Zhen Ling, Hao Li, Bin Zhang
Venue: WSDM
Year: 2025

Website Fingerprinting attack is a type of method used to classify network traffic generated by users on the Tor (The Onion Router) based on the websites they visit, leading to the leakage of individuals' privacy . For Website Fingerprinting attack, network traffic defense methods involve adding noise to the original network traffic to render the attacker's methods ineffective. Previous attack methods primarily focused on improving classification accuracy by enhancing the attack model, with adversarial training being the most common approach. However, adversarial training requires frequent updates and exhibits poor generalization when dealing with previously unseen network traffic protection methods. In order to address the limitations of adversarial training, a novel method is proposed leveraging a diffusion model for network traffic purification. This paper is the first to use a diffusion model to resist network traffic defense based on adversarial perturbations. The diffusion models are theoretically suited for data purification in the training mode, i.e., removing noises generated by adversarial perturbations from the data. Our method enables existing network traffic classification methods to maintain effective classification of network traffic after protection without requiring retraining, while also achieving good generalization performance with previously unseen network traffic defense methods. The purified network traffic data can effectively improve the robustness of existing website fingerprinting methods. Experiments conducted under various network traffic defense strategies demonstrate that the proposed method increases accuracy by up to 60.8% on DF dataset and 50.3% on CW100 dataset, respectively, compared to adversarial training.
UIPN: User Intent Profiling Network for Multi Behavior Modeling in CTR Prediction
Authors: Xu Yang, Guangyuan Yu, Jun He
Venue: WSDM
Year: 2025

Click-through rate (CTR) prediction models often depict a user's interest as a fixed-length vector derived from her historical behaviors, encompassing various types of actions such as clicks, likes, and purchases. Recently, several approaches have been developed to capture users' multiple interests. For accurate multi-behavior prediction, it is essential to represent complex behavior dependencies effectively, as these dependencies are manifested through different behavior types. Advanced multi-behavior models learn relationships among behaviors based on all previous interactions. However, diverse behaviors may indicate different user intentions and unrelated interactions can distract from the target behavior that needs to be predicted. In order to address the limitations highlighted before, we propose a new approach called User Intent Profiling Network (UIPN) for modeling multiple behaviors. UIPN is capable of learning behavior-specific and behavior-dependent intention embedding vectors for users' various behaviors using user intent extractors. These extractors can provide explicit explanations of users' interactions in the online advertising system. The proposed approach has been validated by extensive experiments on public datasets, which illustrate its effectiveness.