Learning from label-limited streams presents significant challenges, particularly when coupled with concept drift and dynamic class imbalance. Existing works often struggle to maintain a discriminative feature space under these constraints, biasing decision boundaries toward majority classes or outdated concepts. To address this, we propose a novel framework named Neural collapse Inspired Label-limited Evolving stream learning (NILE). Instead of utilizing learnable classifiers, NILE exploits Neural Collapse (NC) geometry to explicitly construct a Simplex Equiangular Tight Frame (ETF) as a fixed classifier, ensuring maximal inter-class separability to guide feature discriminability. To maintain this discrimination with limited labels, NILE employs hybrid active learning to prioritize uncertain and minority samples, and an NC-adapted semi-supervised mechanism to enhance representation learning. NILE further updates the classifier by dynamically adjusting the ETF structure, enabling continual adaptation to drifting concepts. Extensive experiments show that NILE can effectively guide features toward the NC state, significantly outperforming state-of-the-art baselines in nonstationary environments.
Hong-Liang Wang, Hong-Yuan Liu, Qi-Rui Hao et al.· Proceedings of the Thirty-Fi...· 0 citations
Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches primarily address catastrophic forgetting by preserving node-related knowledge through replay or distillation techniques; however, they often incur high computational costs and inefficiency. This issue is further exacerbated in real-world scenarios where labeled data for new classes is scarce. In this paper, we propose a novel lightweight plastic-memory framework specifically designed for few-shot incremental learning on graphs. The core idea of our framework is the construction of a plastic-memory module that evolves over time, continuously updating and expanding its memory to accommodate new classes while retaining previously learned knowledge. In contrast to existing techniques, our memory module is both lightweight and effective, featuring an innovative evolving micro-clustering structure that dynamically updates representations of class prototypes, sub-prototypes, and their interaction weights. Building on this memory module, we introduce a memory-driven meta-learning framework that enhances adaptability to new tasks in its inner loop while maintaining stability for earlier tasks in the outer loop. Extensive experiments on four benchmark datasets demonstrate the framework's superior performance in balancing stability for old knowledge and adaptability to new knowledge.
Zi-Han Mei, Zhili Qin, Tongze Zhang et al.· 0 citations
Learning a stable yet highly discriminative representation space that can simultaneously recognize known categories and discover novel ones from limited labeled data is fundamental to Generalized Category Discovery (GCD) on graphs. Recently, Neural Collapse (NC) theory has emerged as a powerful geometric principle for GCD, yielding maximally separated and well-structured class representations by encouraging feature embeddings to converge toward Simplex Equiangular Tight Frame (Simplex ETF) prototypes. However, when extending this paradigm to graph-structured data, a critical challenge arises. Graph representations are inherently shaped by topological dependencies, where neighborhood-based message passing enforces local smoothness among connected nodes. This topology-induced smoothing conflicts with the strict geometric convergence required by Simplex ETF, making neural collapse difficult to realize on graphs. To address this issue, we propose TopoNC, a topology-aware neural collapse framework for Graph GCD. Specifically, it fixes Simplex ETF prototypes as global geometric targets and introduces a Dual-Stream Encoder that decouples topology smoothing from feature-discriminative learning, adaptively balancing the two streams via a gating mechanism. In addition, we further design a Topology-Conditioned Pseudo-Labeling strategy that integrates Sinkhorn-based global balancing, Old-Class Top-K Admission Masking, and Neighborhood-Consensus Screening, to reliably guide feature collapse. Extensive experiments on several benchmark datasets have demonstrated that TopoNC consistently outperforms existing methods, highlighting the importance of topology-aware neural collapse for Graph GCD.
Xi Xu, Zhong Zhang, Hong-Liang Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Learning a stable yet highly discriminative representation space that can simultaneously recognize known categories and discover novel ones from limited labeled data is fundamental to Generalized Category Discovery (GCD) on graphs. Recently, Neural Collapse (NC) theory has emerged as a powerful geometric principle for GCD, yielding maximally separated and well-structured class representations by encouraging feature embeddings to converge toward Simplex Equiangular Tight Frame (Simplex ETF) prototypes. However, when extending this paradigm to graph-structured data, a critical challenge arises. Graph representations are inherently shaped by topological dependencies, where neighborhood-based message passing enforces local smoothness among connected nodes. This topology-induced smoothing conflicts with the strict geometric convergence required by Simplex ETF, making neural collapse difficult to realize on graphs. To address this issue, we propose TopoNC, a topology-aware neural collapse framework for Graph GCD. Specifically, it fixes Simplex ETF prototypes as global geometric targets and introduces a Dual-Stream Encoder that decouples topology smoothing from feature-discriminative learning, adaptively balancing the two streams via a gating mechanism. In addition, we further design a Topology-Conditioned Pseudo-Labeling strategy that integrates Sinkhorn-based global balancing, Old-Class Top-K Admission Masking, and Neighborhood-Consensus Screening, to reliably guide feature collapse. Extensive experiments on several benchmark datasets have demonstrated that TopoNC consistently outperforms existing methods, highlighting the importance of topology-aware neural collapse for Graph GCD.
Xu Xi, Zhong Zhang, Hongliang Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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