OpenRaiser/Intern-Atlas huggingface dataset:
KUG: Joint Enhancement of Internal and External Knowledge for Retrieval-Augmented Generation
Authors: Mingyang Li, Shisong Chen, Shengkun Tu, Ziyi Du, Jinghao Zhang, Zhixu Li, Yanghua Xiao
Venue: CIKM
Year: 2025

Query enhancement, a pivotal methodology in Retrieval-Augmented Generation (RAG) for addressing information scarcity in queries, has garnered increasing research attention. Nevertheless, existing approaches overlook the inherent distinctions between domain-specific knowledge and external factual sources during integration. To bridge this gap, we propose KUG (Knowledge-Update-Generation), a novel RAG framework that leverages internal knowledge semantics to ensure query enhancement efficacy, validates and dynamically updates knowledge representations using external evidence, and achieves systematic integration through knowledge graph embeddings. Extensive experiments on six standard BEIR benchmarks demonstrate that KUG outperforms the state-of-the-art methods, achieving an improvement of 1%-2% in recall metrics. Notably, the framework demonstrates significant performance gains in multi-hop reasoning tasks, advancing the development paradigm for RAG systems. The code will be public soon.
OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
Authors: Bo Li, Yingqi Feng, Ming Jin, Xin Zheng, Yufei Tang, Laurent M. Chérubin, Can Wang, Alan Wee-Chung Liew, Qinghua Lu, Jingwei Yao, Hong Zhang, Shirui Pan, Xingquan Zhu
Venue: CIKM
Year: 2025

Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System, a novel diffusion adversarial framework designed to address these challenges by: (1) employing a transformer-based global dependency capturing module to learn long-range spatio-temporal correlations from sparse trajectories; (2) constructing a generative imputation model that conditions on easily observed tidal covariates to progressively refine imputed salinity fields; and (3) using a scheduler diffusion method to enhance the model's robustness. This unified architecture exploits the periodic nature of tidal signals as a proxy for unmeasured physical drivers, without the need for additional equipment. We evaluate OASIS on four benchmark datasets, including one real-world measurement from Fort Pierce Inlet and three simulated Gulf of Mexico trajectories. Results show consistent improvements over both traditional and neural baselines, achieving up to 52.5% reduction in MAE compared to Kriging. We also develop a lightweight, web-based deployment system that enables salinity imputation through interactive and batch interfaces, available at: https://github.com/yfeng77/OASIS.
Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
Authors: Dawei Li, Yue Huang, Ming Li, Tianyi Zhou, Xiangliang Zhang, Huan Liu
Venue: CIKM
Year: 2025

Generative models such as Large Language Models, Diffusion Models, and generative adversarial networks have recently revolutionized the creation of synthetic data, offering scalable solutions to data scarcity, privacy, and annotation challenges in data mining. This tutorial introduces the foundations and latest advances in synthetic data generation, covers key methodologies and practical frameworks, and discusses evaluation strategies and applications. Attendees will gain actionable insights into leveraging generative synthetic data to enhance data mining research and practice. More information can be found on our website: https://syndata4dm.github.io/.
From Rules to Flexibility: A Resource and Method for SEC Item Extraction in Post-2021 10-K Filings
Authors: Xiao Li, Changhong Jin, Ruihai Dong
Venue: CIKM
Year: 2025

10-K filings represent a significant repository for financial text analysis, encompassing both standardized quantitative indicators and rich unstructured text content. In recent years, the efficacy of rule-based extraction methods has been progressively limited due to changes in the 10-K filing format. In this study, we propose a novel layout-robust segmentation approach that achieves identification of financial report by combining fuzzy matching and structural heuristics. Our approach has been employed in recent 10-K filings (2021-2024), resulting in a standardized dataset with item-level segmentation. Furthermore, an automated validation protocol was developed in order to assess coverage and ranking consistency. Analysis of the protocol indicates that our approach achieves an average extraction accuracy of 87.8%. Finally, a case study utilising Item 1A to forecast short-term stock volatility provides a practical demonstration of the application of the corpus. This case study not only serves to validate the corpus but also showcases its compatibility with EDGAR-CORPUS. Code, benchmarks, segmented 10-K filings, and case studies are publicly available on GitHub. Our GitHub repository: https://github.com/johnny-xiao-li/Flex_10K
Content-Agnostic Moderation for Stance-Neutral Recommendations
Authors: Nan Li, Bo Kang, Tijl De Bie
Venue: CIKM
Year: 2025

Personalized recommendation systems often drive users towards more extreme content, exacerbating opinion polarization. While content-aware moderation has been proposed to mitigate these effects, such approaches risk curtailing the freedom of speech and information. To address this concern, we propose and explore the feasibility of content-agnostic moderation as an alternative approach for reducing polarization. Content-agnostic moderation does not rely on the actual content being moderated, arguably making it less prone to forms of censorship. We establish theoretically that content-agnostic moderation cannot be guaranteed to work in a fully generic setting. However, we show that it can often be effectively achieved in practice with plausible assumptions. We introduce two novel content-agnostic moderation methods that modify recommendations from the content recommender to disperse user-item co-clusters without relying on content features. To evaluate the potential of content-agnostic moderation in controlled experiments, we built a simulation environment to analyze the closed-loop behavior of a system with a given set of users, a recommendation system, and a moderation approach. Through comprehensive experiments in this environment, we show that our proposed moderation methods significantly enhance stance neutrality and maintain high recommendation quality across various data scenarios. Our results indicate that achieving stance neutrality without direct content information is not only feasible but can also help develop more balanced and informative recommendation systems without substantially degrading user engagement.
OFedED: One-shot Federated Learning with Model Ensemble and Dataset Distillation
Authors: Xuhui Li, Zhengquan Luo, Zihui Cui, Xin Cao, Zhiqiang Xu
Venue: CIKM
Year: 2025

One-shot federated learning (FL) has gained traction due to its communication efficiency and scalability. However, unlike traditional FL, which can frequently align client models through multiple rounds of client training and server aggregation, one-shot FL allows only a single communication round, causing each client to easily overfit its local data and leading to divergent objectives. Without any chance to iteratively correct these biases or mitigate heterogeneity, the aggregated model significantly deviates from the optimum achieved under dataset centralized training. To address this challenge, we propose OFedED, a one-shot FL framework that preserves privacy and fully exploits client data by combining local data distillation with server-side ensemble learning. Each client distills its own dataset into an ultracompact coreset that retains essential distributional characteristics; the server aggregates these coresets to guide ensemble training that captures inter-client heterogeneity, harnesses complementary knowledge, corrects local bias, and drives performance close to centralized training. In addition, we theoretically show that, under mild assumptions for local data distillations, the server can simulate a centralized optimization process by finetuning on the aggregated distilled data, effectively bypassing the need for multiple communication rounds, showing that properly distilled data can encode sufficient task-relevant information to support centralized-level optimization. Extensive experiments reveal that OFedED consistently and significantly outperforms SOTA methods, achieving an improvement of up to 9.17% on MNIST and 3.97% on CIFAR-10, the robustness being verified also by experiments using ResNet and various server-client architectures.
Contextual Representation Anchor Network for Mitigating Selection Bias in Few-Shot Drug Discovery
Authors: Ruifeng Li, Wei Liu, Xiangxin Zhou, Mingqian Li, Qiang Zhang, Hongyang Chen, Xuemin Lin
Venue: CIKM
Year: 2025

In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular property prediction. Existing methods for few-shot molecular property prediction overlook the sample selection bias, which arises from non-random sample selection in chemical experiments. This bias in data representativeness leads to suboptimal performance. To overcome this challenge, we present a novel method named Contextual Representation Anchor Network (CRANet), where an anchor refers to a cluster center of the representations of molecules and serves as a bridge to transfer enriched contextual knowledge into molecular representations and enhance their expressiveness. CRANet introduces a dual-augmentation mechanism that includes context augmentation, which dynamically retrieves analogous unlabeled molecules and captures their task-specific contextual knowledge to enhance the anchors, and anchor augmentation, which leverages the anchors to augment the molecular representations. We evaluate our approach using the MoleculeNet and FS-Mol benchmarks, as well as through domain transfer experiments. The outcomes indicate that CRANet surpasses current state-of-the-art methods by 0.10% to 5.48% in AUC and 2.52% in ΔAUC-PR metrics, showcasing its exceptional generalization abilities.
Ensemble Pruning via Graph Neural Networks
Authors: Yuanke Li, Yiyang Liu, Dongmian Zou, Hongfei Wang
Venue: CIKM
Year: 2025

Ensemble learning is a pivotal machine learning strategy that combines multiple base learners to achieve prediction accuracy surpassing that of any individual model. Despite its effectiveness, large-scale ensemble learning consumes a considerable amount of resources. Ensemble pruning addresses this issue by selecting a subset of base learners from the original ensemble to form a sub-ensemble, while maintaining or even improving the performance of the original model. However, existing ensemble pruning strategies often rely on heuristic solutions that may fail to capture complex interactions among base learners. To address this limitation, in this work, we model the base learners in an ensemble as a weighted and attributed graph, where node features represent characteristics of each learner and edge weights represent relationships between the base learners. Leveraging this representation, we propose a novel ensemble pruning method based on graph neural networks (GNNs). Our approach incorporates specialized GNN architectures designed for bagging and boosting ensembles. Experimental results demonstrate that our method not only improves prediction accuracy but also significantly reduces inference time across diverse datasets. Our implementation is available at the anonymous repository: https://github.com/TechnologyAiGroup/GRE.
TKHist: Cardinality Estimation for Join Queries via Histograms with Dominant Attribute Correlation Finding
Authors: Renrui Li, Qingzhi Ma, Jiajie Xu, Lei Zhao, An Liu
Venue: CIKM
Year: 2025

Cardinality estimation has long been crucial for cost-based database optimizers in identifying optimal query execution plans, attracting significant attention over the past decades. While recent advancements have significantly improved the accuracy of multi-table join query estimations, these methods introduce challenges such as higher space overhead, increased latency, and greater complexity, especially when integrated with the binary join framework. In this paper, we introduce a novel cardinality estimation method named TKHist, which addresses these challenges by relaxing the uniformity assumption in histograms. TKHist captures bin-wise non-uniformity information, enabling accurate cardinality estimation for join queries without filter predicates. Furthermore, we explore the attribute independent assumption, which can lead to significant over-estimation rather than under-estimation in multi-table join queries. To address this issue, we propose the dominating join path correlation discovery algorithm to highlight and manage correlations between join keys and filter predicates. Our extensive experiments on popular benchmarks demonstrate that TKHist reduces error variance by 2-3 orders of magnitude compared to SOTA methods, while maintaining comparable or lower memory usage.
Tilia: Enhancing LIME with Decision Tree Surrogates
Authors: Jihang Li, Jiacheng Qiu, Yin-Ping Zhao, Zeyi Wen
Venue: CIKM
Year: 2025

Local Interpretable Model-Agnostic Explanations (LIME) is a widely adopted framework for interpreting opaque models due to its simplicity and intuitiveness. However, LIME suffers from unreliability rooted in two core issues: (i) low fidelity, where the surrogate model fails to accurately approximate the target model's behavior, and (ii) instability, where the generated explanations vary significantly across runs. While prior work has proposed techniques to enhance LIME, they remain fundamentally limited by the expressiveness of linear surrogate models, which cannot adequately capture complex decision boundaries. In this work, we introduce Tilia, a novel method that employs shallow decision tree regressors as the surrogate model, leveraging its structured and deterministic nature to improve both fidelity and stability. Tilia also provides insight into the interplay between surrogate models and sampling strategies, revealing new directions for enhancing explanation reliability. Across extensive experiments on tabular and textual datasets, Tilia outperforms LIME and recent variants on both fidelity and stability, achieving up to 100% approximation of the opaque model and entirely consistent explanations (i.e., 0 Jacard distance). Tilia maintains practical efficiency, completing explanations in seconds even for datasets with over 100 features. These results position Tilia as a robust alternative for model-agnostic explanations. The code is available at https://github.com/neur1n/tilia.
Give Me Some SALT: Structure-Aware Link Modeling for Temporal Weighted Link Prediction
Authors: Ting Li, Hanchen Wang, Yiran Li, Xiaolei Liu
Venue: CIKM
Year: 2025

In dynamic graph analysis, research has predominantly focused on temporal link prediction (TLP) for unweighted links, with growing interest in predicting temporal link weights in recent years. Temporal weighted link prediction (TWLP) aims to estimate both the existence and the link weights, which is naturally formulated as a regression task. The long-tail distribution and short-term randomness of link weights pose significant challenges for TWLP. In this paper, we introduce SALT, a Structure-Aware Link modeling for Temporal weighted link prediction, which consists of Weighted Link Encoder (WLE) and Temporal Link State Space Module (TLSSM). WLE encodes each snapshot into link-centric embeddings with common neighbor information, and addresses the long-tail issue by leveraging weights to adjust the embedding distribution. Additionally, TLSSM is designed to handle short-term randomness in temporal modeling. On eight datasets, our model achieves average reductions of 19.86% in RMSE and 24.61% in MAE compared to state-of-the-art baselines.
VocQuiz: Vocabulary Question Generation for English Language Education
Authors: Yongqi Li, Jiajun Wu, Shangqing Tu, Jifan Yu, Huiqin Liu, Lei Hou, Juanzi Li
Venue: CIKM
Year: 2025

Designing effective English vocabulary question generation tools demands a shift from labor-intensive content creation to large language model (LLM) automation that can adapt to varied educational contexts. Current approaches tend to offer a limited variety of question types, which restricts their practical application in real classroom settings. To better meet the demands of English teaching institutions, we present VocQuiz, a vocabulary question generation system that 1) combines generalization capabilities of LLMs with reliable language resources, including dictionaries, NLP datasets and authentic corpora, to enhance both contextual relevance and linguistic accuracy; 2) supports multiple question types, such as similar word selection and word collocation, to accommodate various instructional requirements; and 3) employs an iterative workflow to iteratively generate and refine questions, ensuring high-quality outputs and consistent assessment standards. VocQuiz offers a practical, deployable solution that helps educators create quiz-based instructional materials, reducing preparation effort while effectively assessing students' mastery of vocabulary.
Improving Rare and Common ICD Coding via a Multi-Agent LLM-Based Approach
Authors: Rumeng Li, Xun Wang, Hong Yu
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have shown strong performance in tasks such as zero- and few-shot information extraction from clinical text without domain-specific training. However, in the ICD coding task, LLMs often hallucinate key details and produce high-recall but low-precision outputs due to the high-dimensional and imbalanced nature of ICD code distributions. Existing LLM-based approaches typically fail to capture the complex, dynamic interactions among human agents involved in real-world coding workflows-such as patients, physicians, and coders-and often lack interpretability and reliability. To address these challenges, we propose a novel multi-agent framework for ICD coding that simulates the real-world process using five role-specific LLM agents-patient, physician, coder, reviewer, and adjuster-and integrates the Subjective, Objective, Assessment, and Plan (SOAP) structure from Electronic Health Records to enhance performance. Evaluated on the MIMIC-III dataset, our method significantly outperforms zero-shot Chain-of-Thought prompting, self-consistency strategies, and LLM-designed agent baselines, particularly for rare codes. Ablation studies confirm the contribution of each agent role, and the system achieves competitive performance with state-of-the-art fine-tuned models, while offering better explainability and requiring no task-specific pre-training.
Differentiable Probabilistic Logic Reasoning For Knowledge Graph Completion
Authors: Zhongbin Li, Lixing Yu, Kun Yue, Xinquan Wu
Venue: CIKM
Year: 2025

Towards Knowledge Graph (KG) completion, probabilistic logic reasoning approaches enable effective rule mining but incur high computational cost, while embedding-based methods offer high efficiency but confront limited semantic understanding. Neuro-symbolic approaches combine both by employing embeddings to approximate probabilistic distributions, yet face challenges in weight optimization and hurdles in scaling up from large probability graphs. To address these issues, we propose DPLogic, a differentiable probabilistic logic reasoning framework for KG completion. Initially, we construct a Markov logic network by selecting crucial formulas and constraining groundings to relevant subgraphs, effectively boosting the scalability of the framework. Subsequently, we represent formula weights through relation-specific embeddings by introducing neural logical operators, creating a differentiable pathway for end-to-end optimization. Finally, we obtain the distribution of unobserved KG triplets by facilitating the joint optimization of embedding-based and probabilistic distributions through an EM algorithm. Empirical findings on standardized datasets illustrate that our proposed DPLogic consistently surpasses state-of-the-art methodologies in terms of both efficacy and efficiency.
Structural Entropy-based Multivariate Time Series Forecasting
Authors: Xinhui Li, Kun Yue, Lixing Yu, Peizhong Yang
Venue: CIKM
Year: 2025

Multivariate time series (MTS) forecasting is crucial for predicting the future states of complexly coupled variables based on historical observations. To effectively capture the intricate interdependencies within MTS, graph-based methods have emerged as powerful tools. However, existing graph construction methods often produce structures that fail to preserve key temporal and cross-variable dependencies, introducing redundant or irrelevant connections. To address these challenges, we propose a structural entropy-based approach for MTS forecasting. The approach optimizes graph structures by reducing structural redundancy, thereby improving forecasting accuracy. Initially, we represent the temporal dependences by constructing an encoding tree incrementally. Through hierarchical organization of time steps, the temporal evolution is adaptively captured. Subsequently, we give the community-aware representation by building an encoding tree over the variables in MTS, extracting homogeneous communities from the tree structure while integrating community influence to better capture inter-variable dependencies. Finally,we present a training algorithm designed to generate accurate predictions for MTS, accompanied by a unified loss function that integrates forecasting inaccuracies with variations in structural entropy. Empirical findings on real-world datasets substantiate that our approach outperforms state-of-the-art models in capturing dependencies and enhancing forecasting precision.
Improving Content Anomaly Detection on Social Media via Counterfactual Mitigation of Social Event-Induced Bias
Authors: Jiaxin Li, Geng Zhao
Venue: CIKM
Year: 2025

Content anomaly detection on social media (SNS) plays a critical role in maintaining healthy online communities. However, the prevalence of major real-world social events can introduce model bias that skews, narrows, and undermines a detector's ability to perceive anomaly in content by contaminating and homogenizing the expressions posted during these events. To address this challenge, we propose SeiNS, a novel, model-agnostic plugin designed to mitigate the bias induced by prevalent social events. SeiNS comprises two components: a Social Event Extractor and an Event Involvement Perceptor. For a given post, SeiNS removes the bias through steps that include, treating the correlation between the label and the social event-induced homogenized content, as well as the correlation between the label and the event-involvement representation, as spurious; performing counterfactual learning on them during finetuning. We evaluate SeiNS on two benchmark datasets composed of real SNS posts under various major social events, where content anomaly respectively plays a negative and a positive role for social good. The results show that SeiNS significantly improves the robustness of SNS content anomaly detectors in dynamic-even unstable-online social environments.