Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 2603-2614· 0 citations· 26 references
Abstract
Graph Domain Incremental Learning (GDIL) aims to acquire knowledge from a continuous stream of graph domains while mitigating catastrophic forgetting. While parameter-isolation methods leveraging graph parameter-efficient adaptation show promise, prompt-based techniques struggle to adapt to GDIL, and low-rank adaptation methods based on a shared classification layer lead to knowledge confusion.Our empirical observations reveal that transferable knowledge is primarily concentrated in the representation layer. Further, we argue that domain-agnostic representations that are not tied to the classification characteristics are needed to assist the new model in capturing more discriminative features for graph domain incremental learning.Motivated by these insights, we propose COllabOrative Knowledge Extraction and integRation (COOKER) method for GDIL to mine inter-domain relationships and uncover the potential of domain-agnostic representations. Specifically, COOKER employs domain-specific LoRA modules and classifiers to capture specific knowledge. A domain-agnostic LoRA module is instantiated to extract transferable knowledge through contrastive acquisition and topology alignment. We introduce collaborative dynamic integration of dual representations to enable adaptive integration, guided by a complementarity loss to eliminate information redundancy. Extensive experiments demonstrate that COOKER significantly outperforms existing baselines, achieving up to a 4.7% improvement in average performance.
This framework performs LLM knowledge elicitation to extract factual knowledge from the model’s internal representations and transforms sentence-level representations into entity-level representations and aligns them within a unified space.
Deyu Chen, Qiyuan Li, Jinguang Gu et al.· 0 citations
Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing methods align representations or regularize graph structures, but do not explicitly model how class-discriminative knowledge learned at different source neighborhood ranges should be routed across target ranges. We call the neighborhood range encoded by a graph representation its propagation resolution and define semantic resolution shift as a cross-domain change in the propagation resolutions at which class-discriminative evidence is strongest. Such shifts can make fixed same-resolution pairing suboptimal and increase the risk of negative transfer. To address this issue, we propose Cross-Resolution Semantic Learning (CReSL), a GDA method that learns soft sourceto-target resolution correspondence from cross-domain class structure. First, CReSL constructs a multi-resolution representation bank using a shared Graph Neural Network and learnable resolution embeddings, with a resolution-indexed expert for each source resolution. Second, CReSL introduces Cross-Resolution Prototype Transport, which constructs class-resolution prototypes from source labels and soft target posteriors and converts cross-domain prototype discrepancies into expert-specific routing over target resolutions. Third, CReSL introduces Cross-Resolution Target Grafting, which constructs posterior-weighted target-to-source prototype displacements and enforces correspondence-weighted prediction consistency for instance-level adaptation under class uncertainty. Extensive experiments on graph benchmarks under diverse domain shifts show that CReSL outperforms strong representative baselines across most settings.
The proposed framework provides a scalable foundation for graph-based artificial intelligence and has applications in biomedical knowledge discovery, financial fraud detection, industrial digital twins, recommendation systems, cybersecurity intelligence, scientific literature mining, and smart governance.
Seppo Linnainmaa, A. Salomaa· International Journal of Eme...· 0 citations
A Propagation-aware Graph Foundation Model (ProGFM), which regards the propagation relationships between edges and feature dimensions as transferable knowledge units, and exhibits superior generalization performance compared with existing methods.
Yi Wang, Jitao Zhao, Di Jin et al.· arXiv.org· 0 citations
Knowledge tracing (KT) is fundamental to intelligent tutoring systems because it models student knowledge evolution and predicts future learning performance. However, existing approaches often struggle to simultaneously capture heterogeneous educational relationships, long-term temporal dependencies, and semantic information embedded in instructional content, limiting predictive accuracy, explainability, and cross-dataset transfer. To address these challenges, this paper proposes MHG-KT (Meta-path-free Heterogeneous Graph Knowledge Tracing), an explainable framework that integrates a meta-path-free heterogeneous graph encoder, Transformer-based temporal modeling, and pretrained Llama 3 8B semantic embeddings through a joint cross-modal attention mechanism. Unlike conventional approaches that learn graph and semantic representations independently before feature fusion, MHG-KT enables continuous interaction between heterogeneous relational and semantic representations during temporal learning, allowing structural, temporal, and semantic dependencies to be jointly optimized within a unified framework. The heterogeneous graph encoder models relationships among students, skills, problems, and contextual features without manually engineered meta-paths, while the Transformer captures knowledge evolution and the semantic encoder enriches graph representations using problem statements and instructional hints. The proposed framework was evaluated on three benchmark datasets (ASSISTments, EdNet, and Junyi) against four representative knowledge tracing models (DIMKT, simpleKT, GIKT, and TGNN). Experimental results demonstrate that MHG-KT consistently outperformed all baselines, achieving AUC scores of 0.927, 0.922, and 0.934 on ASSISTments, EdNet, and Junyi, respectively. Compared with the strongest baseline, the proposed framework improved AUC by up to 1.7%, achieved prediction accuracy of 89.0%, reduced trajectory forecasting error to a minimum MAE of 0.033, maintained calibration errors below 0.061 across all datasets, and achieved inference latency below 5 ms per student interaction.
FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.
Jia-Xin Pan, M. Nayyeri, Osama Mohammed et al.· 0 citations
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