OpenRaiser/Intern-Atlas huggingface dataset:
Extracting Global Temporal Patterns Within Short Look-Back Windows for Traffic Forecasting
Authors: Bo Sun, Zhe Wu, Zhiyuan Deng, Li Su, Qingfang Zheng
Venue: CIKM
Year: 2025

With the continuous expansion of urban areas, accurate and effective traffic forecasting has become essential for intelligent urban traffic management. As traffic data inherently exhibits temporal dynamics, modeling its temporal patterns is critical to improve prediction performance. However, constrained by computational complexity, existing methods rely primarily on short-term historical data, which is typically noisy and limits the ability to capture global temporal patterns. To address this issue, we propose a novel Dual-Stream Transformer model (DSformer) that effectively captures global temporal patterns through a time-index model. To mitigate the impact of noise in short look-back windows, DSformer explicitly learns a temporal matrix that encodes structured temporal dependencies. Furthermore, we design a time-index loss that encourages similar representations for adjacent time indices, thereby reducing error propagation across time steps. In parallel, a historical-value stream is employed to model local information. Finally, a self-adaptive learning module is constructed to flexibly and accurately fuse global and local information. Extensive experiments on real-world traffic forecasting tasks across ten diverse scenarios demonstrate that our method consistently outperforms state-of-the-art baselines while maintaining competitive efficiency. The code is available at https://github.com/sky836/DSFormer.git.
MPFormer: Adaptive Framework for Industrial Multi-Task Personalized Sequential Retriever
Authors: Yijia Sun, Shanshan Huang, Linxiao Che, Haitao Lu, Qiang Luo, Kun Gai, Guorui Zhou
Venue: CIKM
Year: 2025

Modern industrial recommendation systems encounter a core chal- lenge of multi-stage optimization misalignment: a significant semantic gap exists between the multi-objective optimization paradigm (such as jointly optimizing click-through rate, watch duration, and conversion rate) widely used in the ranking phase and the single-objective modeling in the retrieve phase. Although the main-stream industry solution achieves multi-objective coverage throughparallel multi-path single-objective retrieve, this approach leads to linear growth of training and serving resources with the number of objectives and has inherent limitations in handling loosely coupled objectives. This paper proposes the MPFormer, a dynamic multi-task Transformer framework, which systematically addresses the aforementioned issues through three innovative mechanisms. First, an objective-conditioned transformer that jointly encodes user behavior sequences and multi-task semantics through learnable attention modulation; second, personalized target weights are introduced to achieve dynamic adjustment of retrieve results; finally, user personalization information is incorporated into token representations and the Transformer structure to further enhance the model's representation ability. This framework has been successfully integrated into Kuaishou's short video recommendation system, stably serving over 400 million daily active users. It significantly improves user daily engagement and system operational efficiency. Practical deployment verification shows that, compared with traditional solutions, it effectively optimizes the multi-objective retrieve iteration paradigm while maintaining service response speed, providing a scalable multi-objective solution for industrial recommendation systems.
Dynamic Ensemble Member Selection for Data Stream Classification
Authors: Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet
Venue: CIKM
Year: 2025

Ensemble methods are widely recognized for their effectiveness in data stream classification. This paper introduces Dynamic Ensemble Member Selection (DEMS), a novel framework that dynamically selects a subset of classifiers from an ensemble for each individual prediction. DEMS ranks base learners based on estimated accuracy and predictive margin, using only the top-K members for prediction, where K is optimized in a self-adaptive manner. The proposed method significantly enhances predictive performance across various state-of-the-art ensemble algorithms, including Streaming Random Patches, Adaptive Random Forest, and Online Smooth Boost. Experimental results demonstrate that DEMS consistently improves classification accuracy while maintaining a minimal runtime overhead of just 11.66% compared to the original methods. This work highlights the potential of DEMS in adapting to concept drift and optimizing ensemble diversity, offering a practical solution for real-time data stream classification.
Evaluating Differentially Private Generation of Domain-Specific Text
Authors: Yidan Sun, Viktor Schlegel, Srinivasan Kolumam Nandakumar, Iqra Zahid, Yuping Wu, Warren Del-Pinto, Goran Nenadic, Siew-Kei Lam, Jie Zhang, Anil A. Bharath
Venue: CIKM
Year: 2025

Generative AI offers transformative potential for high-stakes domains such as healthcare and finance, yet privacy and regulatory barriers hinder the use of real-world data. To address this, differentially private synthetic data generation has emerged as a promising alternative. In this work, we introduce a unified benchmark to systematically evaluate the utility and fidelity of text datasets generated under formal Differential Privacy (DP) guarantees. Our benchmark addresses key challenges in domain-specific benchmarking, including choice of representative data and realistic privacy budgets, accounting for pre-training and a variety of evaluation metrics. We assess state-of-the-art privacy-preserving generation methods across five domain-specific datasets, revealing significant utility and fidelity degradation compared to real data, especially under strict privacy constraints. These findings underscore the limitations of current approaches, outline the need for advanced privacy-preserving data sharing methods and set a precedent regarding their evaluation in realistic scenarios.
Discovering Group Collapser for Network Resilience
Authors: Guozhang Sun, Haoyuan Wang, Yuhai Zhao, Zhengkui Wang, Yuan Li, Xingwei Wang
Venue: CIKM
Year: 2025

Network resilience refers to the ability of a network to maintain its functionality despite perturbations, where resilience/robustness is shown when a substantial proportion of its nodes remain engaged even under changes. Such phenomenon is common in real-world networks, such as computing power networks. Previous works demonstrate that the coreness of a user/node effectively captures the dynamics of user engagement. However, most existing works only consider changes in a single coreness value and thus fail to measure the overall network resilience. Subsequent works are either inefficient or do not consider coreness-decreased scenario. In this paper, we propose and study the collapsed follower maximization problem, aiming to maximize the number of coreness-decreased vertices by finding a group collapser (collapsing a set of vertices) with a given budget. We prove that the problem is NP-hard and W[2]-hard parameterized by the budget b. To address the problem, we first present a Greedy algorithm that iteratively finds the best collapser in each of the budget b iterations. To further optimize the Greedy algorithm, we propose GreedyOpt, which leverages the shell component structure to accelerate the computation of follower for one collapser and prune the search space. Extensive experimental results on 8 real-world datasets show that the effectiveness and efficiency of our algorithms.
Hearing the Meaning, Not the Mess: Beyond Literal Transcription for Spoken Language
Authors: Min Sun, Ke Xu, Jiarong Liu, Jifan Yang, Yan Fang, Weizheng Wang, Qipeng Xie, Shuxin Zhong, Kaishun Wu
Venue: CIKM
Year: 2025

With the rise of virtual communication and smart devices, speech has become the most natural medium of interaction. Yet it remains intrinsically difficult: speech is fleeting, unstructured, and disfluent, making key information prone to loss. Conventional Speech-to-Text (STT) systems attempt to acoustically reconstruct what was said. However, their frame-level alignment and rigid token-by-token decoding break down under noise, interruptions, or fragmentation. Humans, in contrast, readily grasp what was meant by exploiting syntax, discourse, pragmatics, and prosody. We argue for a paradigm shift from acoustic reconstruction to semantic transduction: inferring meaning directly from speech, abstracted from surface distortions. This shift raises two challenges: (C1) the lack of anchors between audio and meaning, and (C2) the need to maintain compositional semantics. To address these, we introduce CogTrans, a cognitively inspired speech-to-meaning framework. CogTrans tackles C1 through a Semantic Anchor Explorer, built on I-JEPA to capture higher-order regularities, prosodic rhythms, cross-frequency coarticulation, discourse continuity-providing resilient semantic scaffolds under noise and fragmentation. For C2, it designs a Lexical-Semantic Harmonizer that dynamically integrates these anchors with lexical embeddings; thereby preserving fine-grained compositional fidelity in roles, order, and entities. Extensive experiments show that CogTrans delivers consistent and substantial gains under challenging conditions. On GigaSpeech, it achieves a 6.58% relative Word Error Rate (WER) reduction, and on the multilingual VoxPopuli benchmark, the gain climbs to 12.97% at 10 dB noise-a regime where conventional models typically collapse. Beyond literal accuracy, CogTrans also boosts semantic fidelity, with a 3.40% increase in ROUGE-L and 3.45% in USE-Sim, ensuring transcripts remain faithful not only in words but also in meaning. Together, these results underscore that CogTrans is robust in noisy, unconstrained environments-precisely the conditions where reliability matters most.
AMBER: Adaptive Meta Balanced Paradigm for Heterogeneous Graph-Based Knowledge Tracing
Authors: Lifan Sun, Zichen Yuan, Ersheng Ni, Weihua Cheng, Xinyuan Song, Linkun Dai, Hongwei Jiang, Sibo Xu, Mengmeng Chen, Yucen Zhuang, Yongxin Ni, Youhua Li
Venue: CIKM
Year: 2025

Knowledge Tracing (KT) is a fundamental task in personalized education, aiming to predict student performance by modeling their evolving concept mastery. Recent state-of-the-art approaches adopt multi-graph architectures to capture diverse concept and behavior relations. However, such models often suffer from graph imbalance, where one graph branch dominates training, undermining the benefits of structural integration. To address this, we propose AMBER (Adaptive Meta-Balanced Ensemble Representation learning), a KT framework designed to promote balanced learning across heterogeneous graph structures. AMBER introduces an external dual-graph teacher to guide the learning of ensemble representations. As the teacher itself may encode graph imbalance bias, we further incorporate a meta-distillation strategy that adaptively adjusts the teacher using student feedback, amplifying signals beneficial to underperforming branches. In addition, an adaptive graph rebalancing strategy is introduced to balance the optimization of different graph branches in real time, preventing dominance by any single structure. Experiments on three real-world datasets show that AMBER consistently outperforms competitive baselines. By promoting balanced optimization across graphs, AMBER enables more effective integration of heterogeneous learning signals in KT, providing a robust and scalable solution for personalized education. Code is available at https://github.com/AMBER2025KT/AMBER2025CIKM.
Frontiers in Graph Machine Learning for the Large Model Era
Authors: Qingyun Sun, Ziwei Zhang, Xingcheng Fu, Yangqiu Song, Jianxin Li, Philip S. Yu
Venue: CIKM
Year: 2025

The ''Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'25)'' workshop focuses on advancing graph machine learning (GML) techniques in the context of increasingly large and powerful models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning stands at a unique vantage point to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging needs in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.
MMM-fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Trade-offs
Authors: Swati, Arjun Roy, Emmanouil Panagiotou, Eirini Ntoutsi
Venue: CIKM
Year: 2025

Fairness-aware classification requires balancing performance and fairness, often intensified by intersectional biases. Conflicting fairness definitions further complicate the task, making it difficult to identify universally fair solutions. Despite growing regulatory and societal demands for equitable AI, popular toolkits offer limited support for exploring multi-dimensional fairness and related trade-offs. To address this, we present mmm-fair, an open-source toolkit leveraging boosting-based ensemble approaches that dynamically optimizes model weights to jointly minimize classification errors and diverse fairness violations, enabling flexible multi-objective optimization. The system empowers users to deploy models that align with their context-specific needs while reliably uncovering intersectional biases often missed by state-of-the-art methods. In a nutshell, mmm-fair uniquely combines in-depth multi-attribute fairness, multi-objective optimization, a no-code, chat-based interface, LLM-powered explanations, interactive Pareto exploration for model selection, custom fairness constraint definition, and deployment-ready models in a single open-source toolkit, a combination rarely found in existing fairness tools. Demo walkthrough available at: https://youtu.be/_rcpjlXFqkw.
Higher-order Structure and Semantics-enhanced User Profiling for Recommendation
Authors: Yanchao Tan, Xinyi Huang, Zhijun Chen, Hang Lv, Hengyu Zhang, Wei Huang, Guofang Ma
Venue: CIKM
Year: 2025

Accurate user profiles are crucial for personalized recommendation systems to mitigate information overload on large-scale online platforms. While recent advances in large language models have enhanced semantic understanding for profile construction through textual artifacts, existing methods often neglect the higher-order structural patterns inherent in user-item interaction graphs-a key limitation for achieving accurate and diverse recommendations. In this paper, we propose SSPRec, a Higher-order Structure and Semantics-enhanced User Profiling for Recommendation. Specifically, we first introduce a multi-hop proximity matrix over item-item transitions, followed by low-rank approximation and clustering to group users based on behavioral similarity. Group-level user profiles are then distilled via representative keywords extracted from co-interacted items, and collaborative embeddings are concurrently learned from the interaction graph. To integrate collaborative signals with language-based profiles, we introduce a cross-view contrastive objective that encourages coherence between structural and semantic representations. Final recommendations are made using a fused user-item similarity score. Extensive experiments on four real-world datasets show that SSPRec not only outperforms baselines in accuracy (with 46.35% improvements), but also remains diverse and robust, even under incomplete interactions.
Random-Feature Graph Neural Networks with Representation Tokenized Transformer for Robust One-class Anomaly Detection
Authors: Wen-Wei Tang, Cheng-Te Li
Venue: CIKM
Year: 2025

Real-world anomaly detection seldom enjoys abundant, perfectly curated normal data; labels are often limited and untrustworthy, allowing genuine outliers to masquerade as benign. We cast one-class anomaly detection into this harsh setting of label scarcity and noise and present Random-Feature Graph Neural Networks with a Representation Tokenized Transformer (RFGT). RFGT first partitions the feature space into random, non-overlapping subsets, yielding multiple complementary views that dilute the impact of any corrupted dimension. With a graph constructed from each view, a graph learner propagates the sparse normal signal to unlabeled neighbors while tempering mislabeled anomalies. A novel Representation Tokenized Transformer module is learned to capture cross-feature dependencies and implicitly down-weight inconsistent signals within a data instance. Extensive experiments on eight tabular benchmarks exhibit that RFGT outperforms the state-of-the-art as the amount of clean data shrinks or the label noise grows.
On the Cross-type Homophily of Heterogeneous Graphs: Understanding and Unleashing
Authors: Zhen Tao, Ziyue Qiao, Chaoqi Chen, Zhengyi Yang, Lun Du, Qingqiang Sun
Venue: CIKM
Year: 2025

Homophily, the tendency of similar nodes to connect, is a fundamental phenomenon in network science and a critical factor in the performance of graph neural networks (GNNs). While existing studies primarily explore homophily in homogeneous graphs, where nodes share the same type, real-world networks are often more accurately modeled as heterogeneous graphs (HGs) with diverse node types and intricate cross-type interactions. This structural diversity complicates the analysis of homophily, as traditional homophily metrics fail to account for distinct label spaces across node types. To address this limitation, we introduce the Cross-Type Homophily Ratio (CHR), a novel metric that quantifies homophily based on the similarity of target information across different node types. Additionally, we propose Cross-Type Homophily-guided Graph Editing (CTHGE), a novel method for improving heterogeneous graph neural networks (HGNNs) performance by optimizing cross-type connectivity using Cross-Type Homophily Ratio. Extensive experiments on five HG datasets with nine HGNNs validate the effectiveness of CTHGE, which delivers a maximum relative performance improvement of over 25% for HGNNs on node classification tasks, offering a fresh perspective on cross-type homophily in HGs learning.
NuFact: Validating Numerical Assertions for Knowledge Graphs
Authors: Mohammad Taufeeq, Koninika Pal
Venue: CIKM
Year: 2025

Validating extracted assertions is one of the crucial steps for curating knowledge graphs (KGs). While existing research extensively explores methods for validating KG assertions, specifically for the categorical facts -- where both the Subject and Object in triples are entities, there remains a significant gap in the validation of numerical assertions, where the Object represents a quantity. Moreover, general fact-validation methods are inefficient for validating numerical claims due to their limited coverage in KGs. Furthermore, large-language models (LLMs) exhibit limitations in quantitative reasoning, further exacerbating the challenge. These gaps compromise the reliability of KGs in applications that require precise numerical accuracy. Addressing these challenges, we propose NuFact, a framework for validating numerical assertions using evidence gathered from the web. NuFact combines the rich contextual understanding of LLMs with manually crafted quantity-focused and temporal features derived from extracted evidence to assess numerical claims. Experimental evaluations show that NuFact significantly outperforms existing fact-checking baselines and popular LLM-powered agents.
Advances in Medical Knowledge Systems: LLMs, RAG and Foundation Models
Authors: Giulia Di Teodoro, Valerio Guarrasi, Federico Siciliano, Fabrizio Silvestri
Venue: CIKM
Year: 2025

This workshop will explore the latest approaches to medical knowledge systems, with a focus on the synergy between large language models, retrieval-augmented generation, and foundation/agentic models. The workshop will promote interdisciplinary collaboration among researchers, practitioners, and clinicians to advance evidence-driven AI in healthcare. Topics will include knowledge-grounded question answering, biomedical document retrieval, multimodal clinical reasoning, personalization, safety, and the challenges of deploying AI in practice. With a strong emphasis on reproducibility, evaluation, and responsible application in clinical settings, the workshop will define the next frontier of knowledge-centric AI in medicine.
ECG-Doctor: An Interpretable Multimodal ECG Diagnosis Framework Based on Large Language Models
Authors: Dongsheng Tian, Junzhe Jiang, Kai Zhang, Changchun Liu, Yu Yuan, Min Gao, Enhong Chen
Venue: CIKM
Year: 2025

Electrocardiogram (ECG) diagnosis aims to automatically classify ECG recordings into clinically meaningful categories, playing a vital role in medical decision-making. Deep learning methods, while promising, demand extensive annotated data and lack interpretability. Large Language Models (LLMs) offer potential in low-data scenarios and generating interpretable outputs, yet their application to ECG diagnosis, especially leveraging multimodal data (e.g., raw signals, derived features, and clinical knowledge), remains underexplored. To address these challenges, we propose ECG-Doctor, an interpretable and multimodal ECG diagnosis framework based on LLMs. ECG-Doctor comprises four key components: (1) ECG Knowledge Acquisition Module, which integrates external medical knowledge and Chain-of-Thought (CoT) reasoning to address the inability of LLMs to follow standardized ECG diagnostic procedures; (2) ECG Feature Extraction Module, which incorporates domain knowledge to overcome LLMs' limitations in comprehensively understanding structured ECG features; (3) ECG Waveform Analysis Module, which introduces time-series ECG models to equip LLMs with the capability to interpret and reason over raw ECG signal morphologies; (4) KNN-based ECG Retrieval Module, which retrieves the top-k most similar ECG samples and guides LLMs through in-context learning (ICL), enabling them to differentiate and learn from variations across ECGs. The outputs of these modules are aggregated and provided to the LLM as diagnostic context, enabling ICL to perform comprehensive ECG diagnosis. This design effectively simulates the diagnostic reasoning process of experienced electrocardiologists. Extensive experiments on the PTB-XL dataset demonstrate that ECG-Doctor is compatible with various LLMs and consistently outperforms existing baselines at both 100 Hz and 500 Hz sampling rates, showcasing its strong versatility and robustness. Furthermore, ECG-Doctor provides well-grounded diagnostic explanations, highlighting its superior interpretability.