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IJCAI 2026

Manifold-Aligned Rectification Flow for Denoised Social Recommendation

Qing Meng, Zhufu Song, Huiyu Min, Qian Huang, Pengcheng Zhang

Social recommendation leverages social relations to enhance user preference modeling. However, real-world social networks are often noisy and unreliable, where misleading social relationships introduce anisotropic perturbations into user representations. Existing denoising approaches, including heuristic filtering and generative reconstruction, struggle to produce social embeddings that are well aligned with user preferences, limiting their effectiveness in downstream recommendation tasks. To address this challenge, we propose Manifold-Aligned Rectification Flow (MARF), a flow matching based framework that explicitly rectifies noisy social representations toward preference-aligned embeddings. MARF jointly integrates social relations and user-item interactions to construct a preference-aware manifold as the target distribution, guiding the learning of a continuous vector field that captures preference-oriented transformations in the social domain. Through this learned transport process, MARF effectively bridges the gap between the social and preference domains, yielding robust and discriminative user representations. Extensive experiments demonstrate that our proposed model consistently outperforms state-of-the-art social recommendation methods, particularly under sparse and noisy conditions, validating its effectiveness and robustness.

IJCAI 2026

LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation

Jun Ma, Zhenye Yang, Ruichen Zhou, Pei Zhang, Huan Li, Jinpeng Chen

Driver gaze estimation serves as a fundamental metric for evaluating driver attentiveness in modern monitoring systems. Beyond being vulnerable to sudden lighting changes and sensor noise, spatial-domain models struggle to disentangle authentic gaze cues from irrelevant visual attributes. In this paper, we propose LISA, a Language-guided Interference-aware Spatial-Frequency Attention framework that combines frequency-domain priors with vision-language knowledge. Observing that the amplitude spectrum remains relatively stable even under spatial perturbations, we design a dual-domain fusion mechanism. It integrates stable low-frequency semantics into high-frequency details, employing spatial attention to precisely target ocular regions. To reduce semantic ambiguity, we also introduce a training-time disentanglement strategy. Using a frozen CLIP encoder and orthogonal regularization, we explicitly separate gaze features from appearance interference. Experiments on two benchmarks show that LISA achieves state-of-the-art performance, with significantly improved robustness against occlusions and lighting variations. The code repository is available at https://github.com/Mason-bupt/LISA.

IJCAI 2026

Two-Stage Fine-Grained Trajectory Generation Constrained by Road Networks

Zewu Lv, zipei fan, Zhiwen Zhang, Xuan Song

Trajectory generation is a pivotal technique for mitigating data sparsity, but existing methods struggle to simultaneously achieve strict road network alignment and capture realistic movement characteristics. To bridge this gap, we propose RNTrajGen, a two-stage fine-grained trajectory generation framework constrained by road networks. Specifically, we first develop a Road Network Knowledge-Enhanced Encoder (RNKEE) to provide semantically rich representations for trajectory generation. By leveraging graph attention networks, RNKEE encodes static prior knowledge of the road network while integrating self-supervised learning to extract latent movement patterns from real-world trajectories. Subsequently, RNTrajGen follows a two-stage generation strategy: it first generates a sequence of road segments to ensure strict topological alignment, and then infers the moving ratios of trajectory points along road segments to capture fine-grained movement characteristics. Extensive experiments on two real-world datasets demonstrate that RNTrajGen significantly outperforms state-of-the-art baselines, and the generated trajectories exhibit high utility in downstream prediction tasks. Our code is available at https://github.com/jkzh986/RNTrajGen.

IJCAI 2026

JointScaler: A Hierarchical Multi-Indicator Distribution Forecasting Approach for Uncertainty-Aware Joint Scaling in Cloud Services

Yang Luo, Zhemeng Yu, Yikang Fu, Wei Lu, Lintao Ma, Xiaofeng Gao, Guihai Chen

Proactive scaling improves cloud resource efficiency by forecasting system-relevant indicators and dynamically provisioning resources to maximize utilization while satisfying quality requirements. Existing approaches forecast service indicators in isolation, ignore forecasting uncertainty, and scale resource types independently, violating bundled resource constraints and degrading service quality. Therefore, we propose JointScaler, a learning-based framework for multi-indicator distribution forecasting and uncertainty-aware scaling. It captures inter-indicator dependencies via hierarchical attention, models dynamic uncertainties with normalizing flows, and leverages full predictive distributions to optimize bundled resource allocations under quality requirements. Evaluated on 4 real-world datasets, JointScaler improves point and distribution forecasting accuracy by 5.87% and 14.74%, outperforming 12 advanced baselines. In a week-long A/B test on a payment application’s cloud platform, it reduced GPU and CPU usage by 2,400+ and 37,000+ hours with stable service quality, delivering significant economic benefits.

IJCAI 2026

Unsupervised Anomaly Detection in Dynamic Graphs via Compatibility Modeling and Boundary Learning

Jiachi Luo, Shameng Wen, Ziyang Qiu, Chen Jiang, Qi Huang, Yuxing Tian, Aiwen Jiang

Anomaly detection in dynamic graphs is essential for monitoring evolving systems such as transaction networks and online platforms. Yet existing methods remain limited in realistic edge-stream settings: snapshot-based approaches discretize continuous interactions and miss fine-grained temporal signals, while many continuous-time models rely on scarce node/edge attributes and often fail to explicitly assess whether a destination is compatible with a source’s recent context. Moreover, under extreme class imbalance and the lack of anomaly labels, unsupervised detectors frequently yield ill-defined normality criteria and ambiguous decision boundaries. We propose BAD, an unsupervised framework for anomaly detection in continuous-time dynamic graphs. BAD adopts a minimalistic design that represents nodes with learnable identity embeddings and performs pairwise compatibility modeling via cross-attention between each destination node and the source’s recent neighbors, enabling direct characterization of context-dependent deviations without requiring attributes. To obtain a principled separating boundary, BAD further integrates normalizing flows to model the distribution of normal interactions and derive likelihood-based anomaly scores. Extensive experiments on four real-world datasets demonstrate that BAD consistently performs well, highlighting its robustness under feature-scarce and label-scarce conditions.

IJCAI 2026

Learning Hyperspherical Time–Frequency Representations for Time-Series Out-of-Distribution Detection

Willian T. Lunardi, Samridha Shrestha, Martin Andreoni

Out-of-distribution (OOD) detection for time-series data remains comparatively underexplored compared to vision and language, with a limited principled understanding of how supervised time-series representations can be leveraged for reliable detection under distributional shifts. This work formulates time-series OOD detection as representation learning with hyperspherical embeddings, where class-conditional structure is induced by a von Mises–Fisher (vMF) likelihood–based objective on the unit sphere. The learned representation combines time- and frequency-domain views of the input signal via domain-specific encoders, integrating them into a joint embedding space for OOD detection. Detection uses distance-based scores over the learned embeddings, including k-nearest neighbors (k-NN) and Mahalanobis scores. We evaluate the approach at scale on the complete UCR and UEA time-series archives under a cross-dataset protocol. Empirical results show consistent improvements under both k-NN and Mahalanobis scoring over strong contrastive-learning and post-hoc baselines in the same setting. Code is available at https://github.com/tiiuae/hypertf-time-series-ood.

IJCAI 2026

Frequency-Aware Augmentation and Alignment for Time Series Contrastive Learning

Yusen Liu, Zhichen Lai, Hua Lu, Xu Cheng, Xiufeng Liu, Huan Huo

Contrastive learning has become a dominant paradigm for learning time series representations from large-scale unlabeled data. However, current methods are often adapted from computer vision and rely on random time-domain augmentations (e.g., jittering and cropping). Such augmentations can unpredictably disrupt the natural frequency structure of signals, leading to representations failing to capture crucial patterns in the data. To address this, we propose a framework of Frequency-Aware Augmentation and Alignment for Time Series Contrastive Learning (FACL), which comprises two key innovations. First, FACL employs a novel frequency-structured augmentation mechanism based on wavelet transforms. The mechanism constructs controlled and interpretable contrastive views by the structured attenuation and recombination of specific wavelet components. Second, FACL introduces a multi-level contrastive objective that incorporates a subspace alignment strategy. This objective explicitly aligns representations within their corresponding frequency subspaces. Experiments across six forecasting and four classification benchmarks show that FACL achieves superior performance compared to recent baselines. Ablation studies and model analysis highlight the contribution of each component in FACL. Furthermore, low-sample semi-supervised learning experiments confirm the robustness and generalization of FACL.

IJCAI 2026

Resisting Label Drift: Real-Time Multi-View Clustering with Semantic Consistency

Qi Liu, Suyuan Liu, Hao Tan, Yangfan Du, Bowen Zhang, Wenpeng Lu, Xinwang Liu

Real-time clustering of dynamic multi-view data streams is a critical yet challenging task in open-world applications. While several methods have been proposed to address this task, most of them extract features incrementally but fail to output instant clustering results for the current batch. In addition, they neglect semantic consistency, causing the cluster labels of identical concepts to drift unpredictably due to independent processing. To address these limitations, we propose a real-time semantic consistent incremental multi-view clustering framework. Specifically, we constructs a compact historical knowledge base via an adaptive diversity-aware selection mechanism, which guides the clustering of incoming data, enabling immediate inference without accessing the full history. Furthermore, we introduce a semantic alignment strategy based on consensus centers to ensure robust label consistency over time. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.

IJCAI 2026

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification

Pingping Liu, Muyao Wang, Zijian Zhang, Tongshun Zhang, Hao Miao, Guorui Xie, Qingliang Li, Qiuzhan Zhou

Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient. However, existing approaches generally struggle to reconcile fine-grained representation learning, especially under class imbalance and real-world constraints. In this paper, we present FreSH, a Frequency-Segmented Hierarchical Multi-Expert Framework designed to address these challenges. FreSH introduces a new perspective for MTSC by enabling adaptive, multi-scale analysis of temporal signals, allowing different aspects of the data to be modeled in a complementary and coordinated manner. By combining localized specialization with holistic context modeling, FreSH achieves strong representational capacity without incurring excessive computational overhead. An adaptive fusion strategy further enhances flexibility, enabling the model to dynamically emphasize the most informative components of the input. In addition, we incorporate a more robust optimization objective that improves learning stability across varying sample difficulties and class distributions. Extensive evaluations on 30 UEA benchmark datasets and real-world vibration data demonstrate that FreSH consistently outperforms state-of-the-art methods in classification accuracy, while substantially reducing model size and efficiency. The implementation code is publicly available at https://github.com/Wangmy2120/FreSH00.

IJCAI 2026

Graph Label Denoising via Neighborhood Agreement–Guided Expectation Maximization

Dezhi Liu, Richong Zhang, Junfan Chen, Fengbo Tian, Si Chen

Graph Neural Networks are susceptible to label noise, in which message-passing mechanisms serve as conduits for propagating erroneous supervision. Current mitigation techniques typically recover clean labels via heuristics that lack theoretical grounding, which often leads to ineffective denoising. To tackle the issue, we propose NAEM, a latent label estimation framework that models clean labels as latent variables by integrating neighborhood agreement into the Expectation-Maximization (EM) paradigm. To overcome the posterior collapse problem in standard EM under severe noise, we design a structure-aware E-step that leverages neighborhood agreement as a structural prior. This mechanism acts as a dynamic confidence gate based on local consensus to prevent the model from overfitting to noise. Simultaneously, by explicitly modeling the noise transition matrix in the M-step, NAEM decouples noise dynamics from semantic representation learning. Extensive experiments on five benchmark datasets demonstrate that NAEM consistently outperforms SOTA methods under varying noise conditions, validating the effectiveness of our framework.

IJCAI 2026

GraphSculptor: Sculpting Pre-training Core Sets for Graph Self-supervised Learning

Chuang Liu, Zelin Yao, Xueqi Ma, Luzhi Wang, Mukun Chen, Pinghua Xu, Wenbin Hu

Graph self-supervised learning (SSL) typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets contain substantial redundancy—our analysis reveals that uniformly subsampling 50% of graphs retains over 96% of downstream performance. To exploit this redundancy, we introduce GraphSculptor for pre-training coreset construction. Unlike methods dependent on additional training-time signals or limited solely to topological statistics, GraphSculptor provides a label-free solution that constructs coresets via two complementary perspectives: intrinsic structure and contextual semantics. Concretely, structural diversity is quantified using intrinsic graph statistics, yielding a structural feature vector for each graph, while semantic diversity is captured by utilizing a pre-trained language model to encode descriptions generated via graph-to-text. GraphSculptor integrates these signals into a unified metric space and performs cluster-aware selection to preserve joint structural--semantic diversity. We further derive a theoretical bound on the loss gap between coreset and full-data pre-training, offering theoretical motivation for our selection formulation. Extensive experiments demonstrate that GraphSculptor effectively "sculpts" the dataset: a 10% coreset achieves 99.6% of full-data performance while reducing pre-training time by nearly 90%, offering a scalable solution for data-efficient graph pre-training.

IJCAI 2026

DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks

Yan Lin, Jilin Hu, Shengnan Guo, Christian S. Jensen, Youfang Lin, Huaiyu Wan

Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segments as vehicles traverse them. These weights support tasks including traffic microsimulation and vehicle routing with reliability guarantees. We study the problem of time-varying microscopic weight completion. During a time slot, the available weights typically cover only some road segments. Weight completion recovers distributions for the weights of every road segment at the current time slot. This problem involves two challenges: (i) contending with two layers of sparsity, where weights are missing at both the network layer (many road segments lack weights) and the segment layer (a segment may have insufficient weights to enable accurate distribution estimation); and (ii) achieving a weight distribution representation that is closed-form and can capture complex conditions flexibly, including heavy tails and multiple clusters. To address these challenges, we propose DiSGMM that combines sparsity-aware embeddings with spatiotemporal modeling to leverage sparse known weights alongside learned segment properties and long-range correlations for distribution estimation. DiSGMM represents distributions of microscopic weights as learnable Gaussian mixture models, providing closed-form distributions capable of capturing complex conditions flexibly. Experiments on two real-world datasets show that DiSGMM can outperform state-of-the-art methods.

IJCAI 2026

Property Enhanced Instruction Tuning for Multi-Task Molecule Generation with Large Language Models

Xuan Lin, Long Chen, Yile Wang, Yangyang Chen, xiangxiang Zeng

Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annotation for biochemical properties, the performance for molecule generation tasks is still limited, especially for tasks involving multi-properties constraints. In this work, we present a two-step framework PEIT (Property Enhanced Instruction Tuning) to improve LLMs for molecular-related tasks. In the first step, we use textual descriptions, SMILES, and biochemical properties as multimodal inputs to pre-train a model called PEIT-GEN, by aligning multi-modal representations to synthesize instruction data. In the second step, we fine-tune existing open-source LLMs with the synthesized data, the resulting PEIT-LLM can handle molecule captioning, text-based molecule generation, molecular property prediction, and our newly proposed multi-constraint molecule generation tasks. Experimental results show that our pre-trained PEIT-GEN outperforms MolT5, BioT5, MolCA and Text+Chem-T5 in molecule captioning, demonstrating modalities align well between textual descriptions, structures, and biochemical properties. Furthermore, PEIT-LLM shows promising improvements in multi-task molecule generation, demonstrating the effectiveness of the PEIT framework for various molecular tasks. The code and appendix are available at https://github.com/chenlong164/PEIT.

IJCAI 2026

Efficient and Exact Global Attention on Latent Summaries for Knowledge Graph Reasoning

Chenxiao Lin, Lei Wang, Yin Zhang, Wei Liu, Ye Luo, Qingqiang Wu

Capturing global context through attention is essential for reasoning over knowledge graphs, especially when relevant entities are distant or disconnected. To scale attention to large graphs, recent methods replace Softmax with kernel feature mappings, reducing computational complexity to linear in the number of nodes. While efficient, these approximations tend to produce overly smooth attention scores, which can reduce discrimination between correct triplets and hard negative samples. Moreover, they exhibit scale-dependent shifts in attention entropy, making them sensitive to changes in graph size during inductive inference. In this paper, we introduce LaGR, a novel approach for integrating global information in knowledge graph reasoning. Rather than approximating interactions among all nodes, LaGR compresses the graph into a fixed, compact set of latent summaries and applies exact self-attention within this latent space. This change yields scale-invariant attention and stable performance across diverse data settings. In addition, we propose a node-adaptive residual fusion mechanism that dynamically balances local and global information at the node level, leading to more expressive representations. Extensive experiments on both transductive and inductive benchmarks show that LaGR substantially outperforms state-of-the-art baselines, demonstrating that exact attention over latent summaries is an efficient and effective way to capture global context in knowledge graph reasoning. Our implementation is available at https://github.com/XMU-KG/LaGR.

IJCAI 2026

No More Shortcuts: Network Traffic Anomaly Detection via Bidirectional Prediction

Xinglin Lian, Chengtai Cao, Fanglin Yu, Ting Zhong, Fan Zhou

Network Traffic Anomaly Detection (NTAD), particularly under zero-positive settings, is a critical task in cybersecurity. Existing zero-positive NTAD approaches primarily rely on reconstruction-based pipelines. Nevertheless, these methods are susceptible to an identical shortcut issue, where models indiscriminately reconstruct both normal and anomalous inputs, leading to anomaly overgeneralization. To address this limitation, we propose BiPred, the first prediction-based detection paradigm for NTAD. BiPred symmetrically divides each network traffic sample into two segments and reformulates the NTAD task as a bidirectional prediction across these two segments. Unlike reconstruction-based methods, our BiPred not only eliminates shortcut learning by design but also establishes explicit bidirectional contextual dependencies, making it more sensitive to anomalous traffic. Moreover, we develop a novel Residual Scanning Mamba block that encodes multi-view contextual information to support bidirectional prediction. A residual fusion mechanism is proposed for the multi-view scanning Mamba to suppress the accumulation of inter-view redundancy. This design prevents representation degradation and provides multi-view contextual details for prediction. Extensive experiments demonstrate the superiority of our BiPred paradigm, highlighting a new research direction for NTAD. Code is available at https://github.com/ikun0124/BiPred.

IJCAI 2026

Coarse-to-Fine Latent Guidance: A Multi-Scale Diffusion Transformer for Traffic Flow Forecasting

Zetao Li, Silin Zhou, Zheng Hu, Shimin Cai, Tao Zhou

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITS). However, traffic dynamics exhibit inherent multi-scale heterogeneity, where stable global trends are often masked by stochastic local fluctuations. Existing methods struggle to reconcile these conflicting resolutions, leading to sub-optimal forecasting. To address this, we propose the Multi-Scale Spatial-Temporal Diffusion Transformer (MS-STDT). Deviating from previous diffusion approaches that treat generation as a monolithic task, we reframe the forward diffusion process as an intrinsic temporal coarse-graining operation. By leveraging the data's multi-scale hierarchy as a structural anchor, we introduce a coarse-to-fine latent guidance strategy that enables the model to reconstruct stable global trends before refining fine-grained details. This ensures physical consistency and generative stability without requiring external labels. Extensive experiments across six real-world datasets confirm that MS-STDT performs the best, demonstrating significant improvements in predictive accuracy and zero-shot robustness against sensor failures. We provide code and data at https://github.com/ZetaoLiPhD/MS-STDT.

IJCAI 2026

Beyond Homophily: Spectrum-Based Graph Pre-Training and Cluster-Augmented Prompt Tuning

Tingting Li, Yonghao Li, Xiangkun Wang, Sixiang Chen, Boyang Fan, Lingfei Ren, Hao Yu, Xin Yang

Graph pre-training and prompt tuning provide an effective route to label-efficient node classification by learning transferable backbones and adapting them with lightweight prompts. However, existing pre-train-and-prompt pipelines often generalize poorly across graphs with diverse homophily due to two fundamental limitations: (i) spectral bias, where learned representations overemphasize a single frequency band, and (ii) prompt misalignment, where spatial-domain prompts cannot explicitly modulate critical spectral components and may even disrupt frequency-aware backbones. In this paper, we propose SCA-GPPT, a spectrum-aware framework that unifies Spectrum-based Graph Pre-training and Cluster-Augmented Prompt Tuning. First, we present a systematic spectral analysis of graph prompting, revealing the inherent limitations of spatial-domain prompts in capturing attenuated high-frequency signals. Second, we explicitly learn complementary low- and high-frequency spectral filters via parameterized Chebyshev polynomials and employ band-aware contrastive learning to model diverse spectral patterns during pre-training. For downstream adaptation, we freeze the pre-trained backbone and introduce cluster-augmented prompts that directly adapt Chebyshev coefficients while enforcing global structural consistency, enabling stable few-shot transfer across varying homophily regimes. Extensive experiments on real-world benchmarks demonstrate that SCA-GPPT consistently outperforms strong baselines under both transductive and inductive settings. Our code is available at https://anonymous.4open.science/r/SCA-GPPT-17B2.

IJCAI 2026

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts

Peiyuan Li, Yongqi Huang, Jitao Zhao, Dongxiao He, Di Jin, Weixiong Zhang

Heterogeneous Graph Prompt Learning (HGPL) has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream applications in heterogeneous graph settings. However, existing HGPL methods are primarily designed for in-domain scenarios, whereas real-world deployments often span multiple domains, and the data used for pre-training and downstream tasks may originate from different distributions. Consequently, the applicability of current HGPL approaches is limited in in-domain settings, and their performance typically degrades when application domains shift. To address this serious limitation, we develop CHoE, a cross-domain HGPL method built upon an expert network. During pre-training, we introduce and train structure-conditioned experts, and during prompt tuning, we propose a structure-aware expert routing and load balancing mechanism to select structurally compatible experts for each meta-path view. In addition, we design a prompt-based semantic fusion module to integrate representations across views for downstream prediction. Extensive experiments show that CHoE consistently improves performance in few-shot cross-domain applications, outperforming all baseline approaches.

IJCAI 2026

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts

lanhao li, Bingshu Xie, Lijun Sun, Xin Xue, Haoyi Zhou, Jianxin Li

Accurate long-term forecasting in complex systems is frequently compromised by dataset-level distribution shifts, where diverse underlying behavioral modes and evolving system states drive the dynamic multivariate time-series. While existing methods predominantly focus on local temporal shifts, they fail to explicitly model the global structural challenge where datasets are composites of distinct operational regimes. In this paper, we propose NEST, a specialized framework designed to model and recompose these evolving structures through a two-phase dense MoE architecture. NEST first facilitates structural specialization by partitioning the dataset into distinct operational regimes through unsupervised clustering in a principled moment-entropy space. We introduce a regime-oriented router mechanism that generates initial expert weights based on temporal content, subsequently refined through geometric modulation to regime centroids. Crucially, rather than acting as monolithic predictors, individual experts function as specialized kernels that capture regime-specific dynamics by evolving unique variate-attention patterns. Extensive evaluations on diverse benchmarks, including heterogeneous network traffic and physical phenomena, demonstrate that NEST consistently achieves state-of-the-art performance. Our code and datasets are available at \url{https://github.com/Aaralshin/NEST}.

IJCAI 2026

COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs

Sohel Aman Khan, Raghava Mutharaju, Supratim Shit

Knowledge Graphs (KGs) are extensively used across different domains and in several applications. Often, these KGs are very large in size. Such KGs become unwieldy for tasks such as question answering and visualization. Summarization of KGs offers a viable alternative in such cases. Furthermore, personalized KG summarization is crucial in the current data-driven world as it captures the specific requirements of users based on their query patterns. Since it only maintains relevant information, the personalized summaries of KG are small, resulting in significantly smaller storage requirements and query runtime. In this work, we adapt the coreset theory to create personalized KG summaries. For a given dataset and a user-specific query workload, we present an approach that samples a relevant subset of triples using sensitivity-based importance sampling. We ensure that the subset approximates the characteristics of the full dataset with bounded approximation error. We define sensitivity scores that measure the importance of a triple with respect to a user’s query workload, which are then used by our coreset construction algorithm. We explicitly focus on personalized knowledge graph summarization by constructing summaries independently for each user based on their query behaviour. Our evaluation on Freebase, WikiData, and DBpedia shows that COREKG delivers higher query-answering accuracy and structural coverage than the state-of-the-art methods, such as GLIMPSE, PPR, PEGASUS and APEX² while requiring only a tiny fraction of the original graph.

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