OpenRaiser/Intern-Atlas huggingface dataset:
Fairness-Aware Graph Learning: A Benchmark
Authors: Yushun Dong, Song Wang, Zhenyu Lei, Zaiyi Zheng, Jing Ma, Chen Chen, Jundong Li
Venue: KDD
Year: 2025

Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware graph learning methods, which blocks practitioners from choosing appropriate ones for broader real-world applications. In this paper, we present an extensive benchmark on ten representative fairness-aware graph learning methods. Specifically, we design a systematic evaluation protocol and conduct experiments on seven real-world datasets to evaluate these methods from multiple perspectives, including group fairness, individual fairness, the balance between different fairness criteria, and computational efficiency. Our in-depth analysis reveals key insights into the strengths and limitations of existing methods. Additionally, we provide practical guidance for applying fairness-aware graph learning methods in applications. To the best of our knowledge, this work serves as an initial step towards comprehensively understanding representative fairness-aware graph learning methods to facilitate future advancements in this area. Open-source code can be found at: https://github.com/yushundong/Fairness-Aware-Graph-Learning-Benchmark.
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework
Authors: Xin Dong, Sen Jia, Ming Rui Wang, Yan Li, Zhenheng Yang, Bingfeng Deng, Hongyu Xiong
Venue: KDD
Year: 2025

Recently, with the emergence of recent Multimodal Large Language Model (MLLM) technology, it has become possible to exploit its video understanding capability on different classification tasks. In practice, we face the difficulty of huge requirements for GPU resource if we need to deploy MLLMs online. In this paper, we propose COEF-VQ, a novel cascaded MLLM framework designed to enhance video quality understanding on the short-video platform while optimizing computational efficiency. Our approach integrates an entropy-based pre-filtering stage, where a lightweight model assesses uncertainty and selectively filters cases before passing them to the more computationally intensive MLLM for final evaluation. By prioritizing high-uncertainty samples for deeper analysis, our framework significantly reduces GPU usage while maintaining the strong classification performance of a full MLLM deployment. To demonstrate the effectiveness of COEF-VQ, we deploy this new framework onto the video management platform (VMP) at the short-video platform, and perform a series of detailed experiments on two in-house tasks related to video quality understanding. We show that COEF-VQ leads to substantial performance gains from the offline evaluation in these two tasks and effectively enhances platform safety with limit resource consumption, significantly reducing inappropriate content video view rate by 9.9% in a online A/B test without affecting engagement. Post-launch monitoring confirmed sustained improvements, validating its real-world impact.
Stabilizing Modality Gap & Lowering Gradient Norms Improve Zero-Shot Adversarial Robustness of VLMs
Authors: Junhao Dong, Piotr Koniusz, Xinghua Qu, Yew-Soon Ong
Venue: KDD
Year: 2025

Contemporary Vision-Language Models (VLMs) such as CLIP offer an attractive zero-shot classification functionality facilitated by large-scale vision-language pre-training. However, they remain vulnerable to adversarial attacks, a critical security threat in realistic deployment. Adversarially robust fine-tuning provides generalizable robustness on new datasets while preserving natural performance by fine-tuning the pre-trained models. Fine-tuning robust CLIP typically relies on adversaries generated solely from the vision branch. However, this singular focus on the vision modality, coupled with static text prompts used as fixed category prototypes, limits the robustness achieved through dual-modality fine-tuning. We observe for CLIP fine-tuning that zero-shot adversarial robustness improves when we (i) stabilize the modality gap (a phenomenon where image and text features occupy different feature space regions) and (ii) lower/stabilize gradient norms. Both these steps enjoy further improvement of robustness if one fine-tunes with both visual and text adversaries. For both modalities, we leverage (i) the maximization of an effective rank of features and (ii) noise modulation of features. We show that maximizing the effective rank helps lower and stabilize the modality gap over adversaries with varying perturbation radii. The noise modulation of features, achieved by the so-called count sketching, lowers/stabilizes gradient norms. We outperform the state of the art on 15 datasets. We provide the first insights into the effects of modality gap & gradient norms in VLM fine-tuning.
Match: A Maximum-Likelihood Approach for Classification under Label Shift
Authors: Zahra Donyavi, Feiyu Li, Yunrui Zhang, Diego Furtado Silva, Gustavo Batista
Venue: KDD
Year: 2025

Machine learning models often suffer from performance degradation when dealing with class distributions that differ from the training distribution, a scenario commonly referred to as label shift. Addressing this challenge, this paper introduces Match, a novel adjustment approach that maximizes the likelihood of predicted probabilities under class prevalence constraints. Unlike existing methods such as retraining with instance re-weighting and the Bayes update rule, Match ensures that the adjusted class distribution aligns precisely with the prevalence estimates from quantifiers. By formulating the adjustment process as a binary integer linear optimization problem, Match benefits from efficient mixed-integer solvers. Extensive experiments demonstrate that Match outperforms the state-of-the-art in classifier adjustment with statistical significance, particularly in handling scenarios with imbalanced distributions.
Causal Discovery through Synergizing Large Language Model and Data-Driven Reasoning
Authors: Huaming Du, Yujia Zheng, Baoyu Jing, Yu Zhao, Gang Kou, Guisong Liu, Tao Gu, Weimin Li, Carl Yang
Venue: KDD
Year: 2025

Revealing the underlying causal mechanisms in the real world is critical for scientific and technical progress. Despite advancements over the past decades, the lack of high-quality data and the inability of traditional causal discovery algorithms (TCDA) to fully comprehend the exact semantics of variables have long been major obstacles to the broader application of causal discovery. To address this issue, this paper proposes a novel causal modeling framework, LLM-CD, which integrates the metadata-based reasoning capabilities of large language models (LLMs) with the data-driven modeling abilities of TCDA for causal discovery. LLM-CD deeply couples the reasoning abilities of LLMs at various stages of TCDA, and enhances causal discovery through an iterative process. Due to the issues of overconfidence and hallucination in LLMs, LLM-CD quantifies and analyzes its uncertainty by incorporating evidence-based deep learning theory with the assumptions of TCDA. We utilize a large-scale de-identified real patient dataset provided by a hospital, a new dataset extracted from MIMIC-IV about the same disease (lung cancer), and two benchmark datasets to comprehensively evaluate LLM-CD. Extensive experimental results confirm the effectiveness and reliability of LLM-CD, with the highest improvement of 403.93% in the Recall and 25.77% in the Ratio metric across four datasets.
PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
Authors: Haotong Du, Yaqing Wang, Fei Xiong, Lei Shao, Ming Liu, Hao Gu, Quanming Yao, Zhen Wang
Venue: KDD
Year: 2025

With the expansion of business scales and scopes on online platforms, multi-scenario matching has become a mainstream solution to reduce maintenance costs and alleviate data sparsity. The key to effective multi-scenario recommendation lies in capturing both user preferences shared across all scenarios and scenario-aware preferences specific to each scenario. However, existing methods often overlook user-specific modeling, limiting the generation of personalized user representations. To address this, we propose PERSCEN, an innovative approach that incorporates user-specific modeling into multi-scenario matching. PERSCEN constructs a user-specific feature graph based on user characteristics and employs a lightweight graph neural network to capture higher-order interaction patterns, enabling personalized extraction of preferences shared across scenarios. Additionally, we leverage vector quantization techniques to distill scenario-aware preferences from users' behavior sequence within individual scenarios, facilitating user-specific and scenario-aware preference modeling. To enhance efficient and flexible information transfer, we introduce a progressive scenario-aware gated linear unit that allows fine-grained, low-latency fusion. Extensive experiments demonstrate that PERSCEN outperforms existing methods. Further efficiency analysis confirms that PERSCEN effectively balances performance with computational cost, ensuring its practicality for real-world industrial systems.
Bi-Dynamic Graph ODE for Opinion Evolution
Authors: Bowen Duan, Henggang Deng, Jinghua Piao, Huandong Wang, Yue Wang
Venue: KDD
Year: 2025

Modeling opinion dynamics in social networks has been the focus of multiple disciplines in recent decades. Previous studies have often modeled the opinion dynamics as a discrete and homogeneous process, neglecting its continuous and complex nature. To fill this gap, we propose a Bi-Dynamics Graph Ordinary Differential Equation (BDG-ODE) framework, which models complex opinion dynamics as the result of two dynamical processes: the evolution of positive and negative opinions. The proposed model incorporates a dual opinion encoder that processes positive and negative opinions independently. Furthermore, the temporal opinion evolution is modeled through bidirectional graph ordinary differential equations, which allows the model to capture the changes in opinion in continuous time. We introduce an opinion synthesis decoder that effectively maps the evolved representations from the latent space back to the opinion space. Extensive experiments conducted on six datasets with varying characteristics highlight the superiority of BDG-ODE in forecasting opinion evolution within social networks. It achieved an average accuracy improvement of 23.16%, an average enhancement of 29.46% in the F1 score, and an average mean square error of difference improvement of 90. 30%, and an average correlation coefficient improvement of 45.93%, significantly outperforming eight state-of-the-art models. The code for reproduction is available: https://github.com/tsinghua-fib-lab/Bi-Dynamic-Graph-ODE-for-Opinion-Evolution.
The k-Trine Cohesive Subgraph and Its Efficient Algorithms
Authors: Jinyu Duan, Haicheng Guo, Fan Zhang, Kai Wang, Zhengping Qian, Zhihong Tian
Venue: KDD
Year: 2025

In this paper, we introduce and study a novel cohesive subgraph model, named k-trine, to address the defects in the classical k-core and k-truss models. Our analysis shows that the k-trine is a more feasible model for capturing cohesive subgraphs by containing the strongly connected vertices. We analyze the theoretical properties of k-trine and propose efficient algorithms to compute the k-trine. Particularly, we design batch processing algorithms to update the decomposition of k-trine against highly dynamic graphs. Extensive experiments on real-world networks validate the effectiveness of the k-trine model and the efficiency of our algorithms.
Dynamic Localisation of Spatial-Temporal Graph Neural Network
Authors: Wenying Duan, Shujun Guo, Zimu Zhou, Wei Huang, Hong Rao, Xiaoxi He
Venue: KDD
Year: 2025

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings.
ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics
Authors: Georges Dupret, Konstantin Sozinov, Carmen Barcena Gonzalez, Ziggy Zacks, Amber Yuan, Ben Carterette, Manuel Mai, Andrey Gatash, Gwo Liang Lien, Shubham Bansal, Roberto Sanchis-Ojeda, Mounia Lalmas
Venue: KDD
Year: 2025

Making ideal decisions as a product leader in a web-facing company is incredibly challenging. Beyond navigating the ambiguity of customer satisfaction and achieving business goals, leaders must also ensure their products and services remain relevant, desirable, and profitable. Data and experimentation are crucial for testing product hypotheses and informing decisions. Online controlled experiments, such as A/B testing, can provide highly reliable data to support decisions. However, these experiments can be time-consuming and costly, particularly when assessing impacts on key business metrics like retention or long-term value. Offline experimentation allows for rapid iteration and testing but often lacks the same level of confidence and clarity regarding business metrics impact. To address this, we introduce a novel, lightweight, and flexible approach called scenario analysis. This method aims to support product leaders' decisions by using user data and estimates of business metrics. While it cannot fully replace online experiments, it offers valuable insights into trade-offs involved in growth or consumption shifts, estimates trends in long-term outcomes like retention, and can generate hypotheses about relationships between metrics at scale. We implemented scenario analysis in a tool named ForTune. We conducted experiments with this tool using a publicly available dataset and reported the results of experiments carried out by Spotify, a large audio streaming service, using ForTune in production. In both cases, the tool reasonably predicted the outcomes of controlled experiments, provided that features were carefully chosen. We demonstrate how this method was used to make strategic decisions regarding the impact of prioritizing one type of content over another at Spotify.
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures
Authors: Vijay Prakash Dwivedi, Charilaos I. Kanatsoulis, Shenyang Huang, Jure Leskovec
Venue: KDD
Year: 2025

Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data and has been applied to molecules, social networks, recommendation systems, and transportation, among other domains. Data in multi-tabular relational databases can also be constructed as 'relational entity graphs' for Relational Deep Learning (RDL) - a new blueprint that enables end-to-end representation learning without traditional feature engineering. Compared to arbitrary graph-structured data, relational entity graphs have key properties: (i) their structure is defined by primary-foreign key relationships between entities in different tables(ii) the structural connectivity is a function of the relational schema defining a database, and (iii) the graph connectivity is temporal and heterogeneous in nature. In this paper, we provide a comprehensive review of RDL by first introducing the representation of relational databases as relational entity graphs, and then reviewing public benchmark datasets that have been used to develop and evaluate recent GNN-based RDL models. We discuss key challenges including large scale multi-table integration and the complexities of modeling temporal dynamics and heterogeneous data, while also surveying foundational neural network methods and recent architectural advances specialized for relational entity graphs. Finally, we explore opportunities to unify these distinct modeling challenges, highlighting how RDL converges multiple sub-fields in graph machine learning towards the design of foundation models that can transform the processing of relational data.
Normalized Space Alignment: A Versatile Metric for Representation Analysis
Authors: Danish Ebadulla, Aditya Gulati, Ambuj K. Singh
Venue: KDD
Year: 2025

We introduce a manifold analysis technique for neural network representations. Normalized Space Alignment (NSA) compares pairwise distances between two point clouds derived from the same source and having the same size, while potentially possessing differing dimensionalities. NSA can act as both an analytical tool and a differentiable loss function, providing a robust means of comparing and aligning representations across different layers and models. It satisfies the criteria necessary for both a dissimilarity metric and a neural network loss function. We showcase NSA's versatility by illustrating its utility as a representation space analysis metric and a structure-preserving loss function. NSA is not only computationally efficient, but it can also approximate the global structural discrepancy during mini-batching, facilitating its use in a wide variety of neural network training paradigms.
Fragile Earth: Innovative AI For Climate Risk Mitigation
Authors: Emre Eftelioglu, Naoki Abe, Ramakrishnan Kannan, Yuzhou Chen, Kathleen Buckingham, Auroop R. Ganguly, James Hodson
Venue: KDD
Year: 2025

The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.
Quick Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms
Authors: Derek Everett, Fred Lu, Edward Raff, Fernando Camacho, James Holt
Venue: KDD
Year: 2025

Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed methods typically relax only one of these assumptions: existing non-stationary bandit policies are designed for a small number of arms, while Lipschitz, linear, and Gaussian process bandit policies are designed to handle a large (or infinite) number of arms in stationary reward environments under constraints on the reward function. In this manuscript, we propose a novel policy to learn reward environments over a continuous space using Gaussian interpolation. We show that our method efficiently learns continuous Lipschitz reward functions with O*(√T) cumulative regret. Furthermore, our method naturally extends to non-stationary problems with a simple modification. We finally demonstrate that our method is computationally favorable (100-10000x faster) and experimentally outperforms sliding Gaussian process policies on datasets with non-stationarity and an extremely large number of arms.
Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest
Authors: Zhibo Fan, Hongtao Lin, Haoyu Chen, Bowen Deng, Hedi Xia, Yuke Yan, James Li
Venue: KDD
Year: 2025

Industrial recommendation systems are typically composed of multiple stages, including retrieval, ranking, and blending. The retrieval stage plays a critical role in generating a high-recall set of candidate items that covers a wide range of diverse user interests. Effectively covering the diverse and long-tail user interests within this stage poses a significant challenge: traditional two-tower models struggle in this regard due to limited user-item feature interaction and often bias towards top use cases. To address these issues, we propose a novel multi-embedding retrieval framework designed to enhance user interest representation by generating multiple user embeddings conditioned on both implicit and explicit user interests. Implicit interests are captured from user history through a Differentiable Clustering Module (DCM), whereas explicit interests, such as topics that the user has followed, are modeled via Conditional Retrieval (CR). These methodologies represent a form of conditioned user representation learning that involves condition representation construction and associating the target item with the relevant conditions. Synergizing implicit and explicit user interests serves as a complementary approach to achieve more effective and comprehensive candidate retrieval as they benefit on different user segments and extract conditions from different but supplementary sources. Extensive experiments and A/B testing reveal significant improvements in user engagements and feed diversity metrics. Our proposed framework has been successfully deployed on Pinterest home feed.
FedVS: Towards Federated Vector Similarity Search with Filters
Authors: Zeheng Fan, Yuxiang Zeng, Zhuanglin Zheng, Binhan Yang, Yongxin Tong
Venue: KDD
Year: 2025

Vectors are used to represent unstructured data with their embeddings and associated attributes. Similarity search over large-scale vector datasets has gained significant interest from both industry and academia. It aims to identify the k nearest neighbors to a query object from vectors that satisfy a given attribute filter constraint. Despite its popularity, most solutions focus on single-sourced data and overlook the need for vector retrieval across federated datasets. To fill this gap, we introduce a new problem, federated vector similarity search with filters, which enables privacy-preserving vector retrieval over multi-sourced data held by mutually untrusted providers. While some solutions can be adapted, they struggle with low recall, excessive search latency, or high communication cost. To address these challenges, we propose FedVS, a privacy-preserving framework enhanced with indexing and pruning based on Trusted Execution Environment (TEE). We also provide a comprehensive theoretical analysis, including complexity, security, and approximation guarantees for recall. Moreover, we deploy our solution over real-world vector databases and conduct extensive experiments. The results demonstrate that our solution outperforms state-of-the-art methods in both effectiveness and efficiency.