OpenRaiser/Intern-Atlas huggingface dataset:
Gravity-GNN: Deep Reinforcement Learning Guided Space Gravity-based Graph Neural Network
Authors: Huaming Wu, Lei Tian, Chaogang Tang, Pengfei Jiao, Minxian Xu, Huijun Tang
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities in handling graph data. Typically, GNNs recursively aggregate node information, including node features and local topological information, through a message-passing scheme. However, most existing GNNs are highly sensitive to neighborhood aggregation, and irrelevant information in the graph topology can lead to inefficient or even invalid node embeddings. To overcome these challenges, we propose a novel Space Gravity-based Graph Neural Network (Gravity-GNN) guided by Deep Reinforcement Learning (DRL). In particular, we introduce a novel similarity measure called ''node gravity'', inspired by the gravitational force between particles in space, to compare nodes within graph data. Furthermore, we employ DRL technology to learn and select the most suitable number of adjacent nodes for each node. Our experimental results on various real-world datasets demonstrate that Gravity-GNN outperforms state-of-the-art methods regarding node classification accuracy, while exhibiting greater robustness against disturbances.
Trustworthy Knowledge Discovery and Data Mining (TrustKDD)
Authors: Le Wu, Jindong Wang, Ling Chen, Xiangyu Zhao, Kui Yu, Yashar Deldjoo, Defu Lian
Venue: CIKM
Year: 2025

The explosion of data and the widespread adoption of AI techniques, especially the success of foundation models and generative AI, have transformed knowledge discovery and data mining (KDD), making them integral to real-world decision-making. For both traditional AI methods and generative AI, issues such as data noise, algorithmic bias, lack of interpretability, and privacy concerns can significantly impact the quality and reliability of extracted knowledge, thereby affecting downstream decision-making. This workshop aims to bring together researchers and practitioners from information and knowledge management, data mining, and intelligent systems to explore trustworthy KDD across diverse settings in the generative AI era. We welcome contributions on robust data preprocessing, explainable learning algorithms, bias detection and mitigation, secure and privacy-preserving mining, trustworthy knowledge graph construction, resource-efficient deployment, alignment of foundation models, and applications for social good. Special emphasis is placed on emerging challenges posed by large-scale, pre-trained models in dynamic, multi-source, and user-centric environments. By fostering dialogue between traditional KDD approaches and innovations in the foundation model era, TrustKDD seeks to advance trustworthy methodologies that align with CIKM's mission of developing reliable, scalable, and intelligent information and knowledge systems.
TLCCSP: A Scalable Framework for Enhancing Time Series Forecasting with Time-Lagged Cross-Correlations
Authors: Jianfei Wu, Wenmian Yang, Bingning Liu, Weijia Jia
Venue: CIKM
Year: 2025

Time series forecasting is critical across various domains, such as weather, finance and real estate forecasting, as accurate forecasts support informed decision-making and risk mitigation. While recent deep learning models have improved predictive capabilities, they often overlook time-lagged cross-correlations between related sequences, which are crucial for capturing complex temporal relationships. To address this, we propose the Time-Lagged Cross-Correlations-based Sequence Prediction framework (TLCCSP), which enhances forecasting accuracy by effectively integrating time-lagged cross-correlated sequences. TLCCSP employs the Sequence Shifted Dynamic Time Warping (SSDTW) algorithm to capture lagged correlations and a contrastive learning-based encoder to efficiently approximate SSDTW distances. Experimental results on weather, finance and real estate time series datasets demonstrate the effectiveness of our framework. On the weather dataset, SSDTW reduces mean squared error (MSE) by 16.01% compared with single-sequence methods, while the contrastive learning encoder (CLE) further decreases MSE by 17.88%. On the stock dataset, SSDTW achieves a 9.95% MSE reduction, and CLE reduces it by 6.13%. For the real estate dataset, SSDTW and CLE reduce MSE by 21.29% and 8.62%, respectively. Additionally, the contrastive learning approach decreases SSDTW computational time by approximately 99%, ensuring scalability and real-time applicability across multiple time series forecasting tasks.
Learning Conditional Probability Distributions for Robust Probabilistic Inference in Bayesian Network
Authors: Xinran Wu, Kun Yue, Huashuai Liu, Liang Duan
Venue: CIKM
Year: 2025

Bayesian Network (BN) has been widely employed for many applications like medical diagnosis due to its ability to deal with probabilistic inferences. Real-world inference tasks in BN cannot be robustly processed by classic search-based inference algorithms, since the conditional probabilities w.r.t. given arbitrary evidence values may be missing (i.e., not included) in the conditional probability tables (CPTs). Most of the existing methods, relying on imputation models, density estimation models or deep neural networks, cannot accurately learn these missing probabilities. To this end, we incorporate the idea of learning and search for robust probabilistic inferences in BN. Firstly, we decompose the probabilistic inference task into missing and existing probability factors, ensuring the consistency of their probability spaces. Secondly, we define the Wasserstein distance between missing and existing probability factors, and incorporate the idea of generative adversarial network to obtain missing probability factors with the minimal Wasserstein distance. Finally, we give the algorithm for robust probabilistic inferences with arbitrary evidence values, which could also be used to deal with the probabilistic inferences with arbitrary query values. Extensive experiments on synthetic and real-world datasets are conducted to demonstrate the superiority of our proposed method.
Fact or Facsimile? Evaluating the Factual Robustness of Modern Retrievers
Authors: Haoyu Wu, Qingcheng Zeng, Kaize Ding
Venue: CIKM
Year: 2025

Dense retrievers and rerankers are central to retrieval-augmented generation (RAG) pipelines, where accurately retrieving factual information is crucial for maintaining system trustworthiness and defending against RAG poisoning. However, little is known about how much factual competence these components inherit or lose from the large language models (LLMs) they are based on. We pair 12 publicly released embedding checkpoints with their original base LLMs and evaluate both sets on a factuality benchmark. Across every model evaluated, the embedding variants achieve markedly lower accuracy than their bases, with absolute drops ranging from 12 to 43 percentage points (median 28 pts) and typical retriever accuracies collapsing into the 25-35 % band versus the 60-70 % attained by the generative models. This degradation intensifies under a more demanding condition: when the candidate pool per question is expanded from four options to one thousand, the strongest retriever's top-1 accuracy falls from 33 % to 26 %, revealing acute sensitivity to distractor volume. Statistical tests further show that, for every embedding model, cosine-similarity scores between queries and correct completions are significantly higher than those for incorrect ones (p < 0.01), indicating decisions driven largely by surface-level semantic proximity rather than factual reasoning. To probe this weakness, we employed GPT-4.1 to paraphrase each correct completion, creating a rewritten test set that preserved factual truth while masking lexical cues, and observed that over two-thirds of previously correct predictions flipped to wrong, reducing overall accuracy to roughly one-third of its original level. Taken together, these findings reveal a systematic trade-off introduced by contrastive learning for retrievers: gains in semantic retrieval are paid for with losses in parametric factual knowledge, and the resulting models remain highly vulnerable to adversarial or even benign rephrasings. Our study underscores the need for retrieval objectives that balance similarity with factual fidelity to safeguard next-generation RAG systems against both misinformation and targeted attacks.
Beyond Return Conditioning: Multi-Scale Sequence Modeling and Advantage-Guided Policy Routing for Offline RL
Authors: Kunbao Wu, Xinning Zhu, Yang Qin, Tieru Wang, Jianzhou Diao, Zheng Hu
Venue: CIKM
Year: 2025

Return-conditioned supervised learning (RCSL) in offline reinforcement learning (RL) leverages Transformers to extract behavioral patterns from offline datasets for decision-making. However, it suffers from inherent limitations in comprehensively capturing multi-scale temporal relationships in historical trajectories. Moreover, its return-conditioning mechanism offers limited guidance in exploiting high-quality behavioral patterns, often resulting in suboptimal action generation during inference. To address these challenges, we propose the Advantage Decision ConvMamba (ADCM), a method that integrates multi-scale sequence modeling (MSSM) with advantage policy guidance (APG). ADCM reconstructs historical sequences through patch partitioning and employs Mamba architecture together with causal convolutions to model sparse global dependencies and dense local Markovian dependencies for behavioral pattern discovery. By incorporating relative advantage action sampling based on the Mixture-of-Experts (MoE) framework, ADCM prioritizes high-quality actions during inference, thereby reducing reliance on low-quality behavioral patterns in the dataset. We evaluate ADCM on multiple offline RL benchmarks from D4RL. Experimental results show that ADCM achieves significant improvements over baseline models, with particularly strong performance on suboptimal datasets. The code for ADCM is available at https://github.com/iTom233/ADCM.git.
FedGC: Contrastive-enhanced Subgraph Federated Learning with Grouping Pseudo-Label
Authors: Keao Xi, Nannan Wu, Yiming Zhao, Wenjun Wang
Venue: CIKM
Year: 2025

Graph structures are widely used to model relational data. In many real-world applications, each data client usually holds only a partial subgraph of the original graph, and privacy concerns limit data exchange between clients. This decentralized data distribution often degrades the effectiveness of conventional Graph Neural Networks (GNNs). Recently, subgraph federated learning has been proposed to enable subgraph data collaborative training without compromising privacy. However, two critical challenges remain: (1) Missing links between subgraphs from different clients significantly prevent the message-passing process in GNNs. (2) Varying data distributions across subgraphs lead to the non-IID issue (e.g., node label skews), which require personalization of local clients in subgraph federated learning scenarios. To address these challenges, we propose FedGC, a novel subgraph federated method that combines pseudo label-based client grouping with Local-Global Contrastive Tasks. Specifically, FedGC initializes a random graph on the server, leverages predicted pseudo label distributions to group clients, and assigns aggregation weights based on the similarity of these distributions. Additionally, FedGC incorporates Local-Global Contrastive tasks into the local client learning process to achieve personalized client parameter update. By adjusting the balance between local supervision task and contrastive task, FedGC enables each client to effectively control the balance of local and global information. Extensive experiments on six real-world datasets that cover citation networks and social networks validate the superior performance of FedGC compared to state-of-the-art baselines.
StoryWriter: A Multi-Agent Framework for Long Story Generation
Authors: Haotian Xia, Hao Peng, Yunjia Qi, Bin Xu, Juanzi Li, Lei Hou, Xiaozhi Wang
Venue: CIKM
Year: 2025

Long story generation remains a challenge for existing large language models (LLMs), primarily due to two main factors: (1) discourse coherence, which requires plot consistency, logical coherence, and completeness in the long-form generation, and (2) narrative complexity, which requires an interwoven and engaging narrative. In this paper, we present StoryWriter, a modular and open-source multi-agent framework for controllable and scalable long story generation. We conduct both human and automated evaluation, and StoryWriter significantly outperforms existing story generation baselines in both story quality and length. Furthermore, we use StoryWriter to generate a dataset, which contains about 6,000 high-quality long stories, with an average length of 8,000 words. We train the model Llama3.1-8B and GLM4-9B using supervised fine-tuning on LongStory and develop StoryWriterLLAMA and StoryWriterGLM, which demonstrates advanced performance in long story generation. All code, models, and data are made publicly available to encourage further development.
GraphIAM: Two-Stage Algorithm for Improving Class-Imbalanced Node Classification on Attribute-Missing Graphs
Authors: Riting Xia, Chunxu Zhang, Xueyan Liu, Anchen Li, Yan Zhang
Venue: CIKM
Year: 2025

Addressing class-imbalanced graphs is a challenging task due to the involvement of both node attributes and graph structures. Existing works on class-imbalanced graphs simply assume that all node attributes are available. However, in real-world graphs, many nodes may lack attributes due to privacy issues or missing data, making class-imbalanced graph learning more challenging. In this paper, we propose GraphIAM, a novel two-stage algorithm for improving class-imbalanced node classification on attribute-missing graphs. In the pre-training phase, GraphIAM adopts graph contrastive learning with oversampling to tackle both attribute-missing and class-imbalanced issues. During fine-tuning, an adapter mechanism is introduced to learn node representations, alleviating the generalization gap between pre-training and downstream tasks. Experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance, outperforming class-imbalanced graph learning approaches by 5% in F Score on graphs with severe attribute missingness.
Harnessing Light for Cold-Start Recommendations: Leveraging Epistemic Uncertainty to Enhance Performance in User-Item Interactions
Authors: Yang Xiang, Li Fan, Chenke Yin, Menglin Kong, Chengtao Ji
Venue: CIKM
Year: 2025

Most recent paradigms of generative model-based recommendation still face challenges related to the cold-start problem. Existing models addressing cold item recommendations mainly focus on acquiring more knowledge to enrich embeddings or model inputs. However, many models do not assess the efficiency with which they utilize the available training knowledge, leading to the extraction of significant knowledge that is not fully used, thus limiting improvements in cold-start performance. To address this, we introduce the concept of epistemic uncertainty (which refers to uncertainty caused by a lack of knowledge of the best model) to indirectly define how efficiently a model uses the training knowledge. Since epistemic uncertainty represents the reducible part of the total uncertainty, we can optimize the recommendation model further based on epistemic uncertainty to improve its performance. To this end, we propose a Cold-Start Recommendation based on Epistemic Uncertainty (CREU) framework. Additionally, CREU is inspired by Pairwise-Distance Estimators (PaiDEs) to efficiently and accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The proposed method is evaluated through extensive offline experiments on public datasets, which further demonstrate the advantages and robustness of CREU. The source code is available at https://github.com/EsiksonX/CREU.
Dual-Space Masked Reconstruction for Robust Self-Supervised Human Activity Recognition
Authors: Shuo Xiao, Jiukai Deng, Chaogang Tang, Zhenzhen Huang
Venue: CIKM
Year: 2025

Human Activity Recognition (HAR) based on wearable sensors faces critical challenges, including limited labeled data, distribution shifts, and sensitivity to sensor noise. To address these issues, this paper proposes a novel self-supervised learning (SSL) framework that leverages dual-space masked reconstruction to learn robust and generalizable representations for sensor-based HAR. Specifically, we design a brand-new pretext task in the pre-training stage, which learns representations by reconstructing the original signals with partial masks in both the original space and the latent space. For the reconstruction task in the original space, we adopt an approach similar to Masked Autoencoders (MAE). In the latent space, we introduce a Spectral block to extract more discriminative representations from the time-domain and frequency-domain information. Then, we implement feature-level reconstruction using a Mean Teacher Network. The feature extractor trained through this pretext task is subsequently utilized for downstream challenging HAR tasks. Experiments on four public datasets (MotionSense, UCI-HAR, PAMAP2 and RealWorld) demonstrate the framework's superiority over state-of-the-art SSL methods, achieving an average 2.45% improvement in Macro F1-score under fine-tuning protocols. This work achieves excellent activity recognition performance in labeled data scarcity and Non- IID data scenarios that are closer to the display world by unifying local and global signal interaction. It provides a scalable solution for anti noise activity recognition in heterogeneous environments, thereby advancing HAR.
Efficient Knowledge Graph Unlearning with Zeroth-order Information
Authors: Yang Xiao, Ruimeng Ye, Bohan Liu, Xiaolong Ma, Bo Hui
Venue: CIKM
Year: 2025

Due to regulations like the Right to be Forgotten, there is growing demand for removing training data and its influence from models. Since full retraining is costly, various machine unlearning methods have been proposed. In this paper, we firstly present an efficient knowledge graph (KG) unlearning algorithm. We remark that KG unlearning is nontrivial due to the distinctive structure of KG and the semantic relations between entities. Also, unlearning by estimating the influence of removed components incurs significant computational overhead when applied to large-scale knowledge graphs. To this end, we define an influence function for KG unlearning and propose to approximate the model's sensitivity without expensive computation of first-order and second-order derivatives for parameter updates. Specifically, we use Taylor expansion to estimate the parameter changes caused by data removal. Given that the first-order gradients and second-order derivatives dominate the computational load, we use the Fisher matrices and zeroth-order optimization to approximate the inverse-Hessian vector product without constructing the computational graphs. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art graph unlearning baselines significantly in terms of unlearning efficiency and unlearning quality. Our code is released at https://github.com/NKUShaw/ZOWFKGIF.
Frequency-Domain Disentanglement-Fusion and Dual Contrastive Learning for Sequential Recommendation
Authors: Shuo Xiao, Jingtao Zhang, Chaogang Tang, Zhenzhen Huang
Venue: CIKM
Year: 2025

Sequential recommendation(SR) aims to provide personalized recommendations by capturing behavioral intents from existing user interaction sequences. Most previous studies are based on attention mechanisms; however, these approaches suffer from inherent over-smoothing issues that limit their ability to capture transient behavioral signals reflecting the user's immediate intents in interaction sequences. Recently, frequency-domain analysis methods based on the Fourier transform have garnered significant attention in the sequential recommendation domain. By applying the Fourier transform, interaction sequences can be mapped to the frequency domain, enabling direct analysis and targeted manipulation of distinct frequency components. In addition to the inherent limitations of self-attention mechanisms, sequential recommendation faces persistent challenges such as data sparsity and noise. To address these issues, we propose Frequency-Domain Disentanglement-Fusion and Dual Contrastive Learning for Sequential Recommendation (FDCLRec). FDCLRec replaces self-attention mechanisms with a frequency-domain adaptive filtering module, which decouples sequence patterns into distinct high-/low-frequency components and synthesizes comprehensive sequence representations through adaptively weighted fusion. In addition, two auxiliary contrastive learning tasks(augmented-view contrasting and same-target sequence contrastive learning) are strategically integrated to alleviate data sparsity and interaction noise. Extensive experiments on four real-world datasets demonstrate that our model outperforms baseline methods.
Lead-LagNet: Exploiting Lead-Lag Dependencies for Cross-Series Temporal Prediction
Authors: Zhilong Xie, Shaofei Shen, Jiwen Huang, Rui Cheng, Qing Li
Venue: CIKM
Year: 2025

In many real-world systems, the evolution of one time series often leads or lags that of its related peers rather than moving in perfect synchrony. Graph Neural Networks (GNNs) are widely used to model such inter-Series Lead-Lag relationships, representing entities as graph nodes with time-series attributes. However, existing methods typically collapse temporal information into discrete points and adopt uniform messaging mechanisms assuming synchronized upward/downward effects and identical time lags among related peers, which are often inconsistent with real-world dynamics. Furthermore, stacking GNN layers to capture multi-hop influences reduces interpretability, hindering understanding of underlying dynamics. To address these issues, we propose the Lead-LagNet, a framework designed to capture diverse cross-series propagation patterns with lead-lag phenomenon in time series. The Lead-LagNet identifies meaningful subsequences in time series and employs a gating mechanism to establish lead-lag connections, enabling the model to uncover complex influencing patterns without relying on predefined relationships. By decoupling the linear messaging process from non-linear feature extraction, the proposed Lead-LagNet enhances both modeling flexibility and interoperability. Experimental evaluation of both synthetic tasks and real-world datasets demonstrates the superiority of Lead-LagNet over state-of-the-art algorithms, including BiGRU, SFM, TGC, FinGAT and ADGAT. Our code and data are available at https://github.com/FICLAB/LeadLagNet.