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- Beyond Low-frequency Information in Graph Convolutional Networks868
An experimental investigation assessing the roles of low-frequency and high-frequency signals is presented, and a novel Frequency Adaptation Graph Convolutional Networks (FAGCN) with a self-gating mechanism is proposed, which can adaptively integrate different signals in the process of message passing.
- Structural Deep Clustering Network727
A Structural Deep Clustering Network (SDCN) is proposed to integrate the structural information into deep clustering, with a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model.
- A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources480
This survey presents several widely deployed systems that have demonstrated the success of HG embedding techniques in resolving real-world application problems with broader impacts and summarizes the open-source code, existing graph learning platforms and benchmark datasets.
- Specformer: Spectral Graph Neural Networks Meet Transformers176
This work introduces Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain, leading to a learnable set-to-set spectral filter and design a decoder with learnable bases to enable non-local graph convolution.
- A Survey on Spectral Graph Neural Networks42
It is shown that spectral GNNs can capture global information and have better expressiveness and interpretability, and is classified according to the spectrum information they use, \ie, eigenvalues or eigenvectors.
- Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations42
This paper analyzes two representative semi-supervised learning algorithms: label propagation (LP) and consistency regularization (CR) and finds that LP utilizes the prior knowledge of graphs to improve consistency and CR adopts variable augmentations to promote diversity.
- Data-Centric Graph Learning: A Survey39
A novel taxonomy based on the stages in the graph learning pipeline is proposed, and the processing methods for different data structures in the graph data are highlighted, i.e., topology, feature and label.
- Graph Contrastive Learning with Stable and Scalable Spectral Encoding23
This work designs an informative, stable, and scalable spectral encoder, termed EigenMLP, and proposes a spatial-spectral contrastive framework (Sp 2 GCL) to capture the consistency between the spatial information encoded by graph neural networks and the spectral information learned by EigenMLP, thus effectively fusing these two graph views.
- Graph Distillation with Eigenbasis Matching17
Eigenbasis matching for spectrum-free graph condensation is proposed, named GCEM, which has two key steps: First, GCEM matches the eigenbasis of the real and synthetic graphs, rather than the graph structure, which eliminates the spectrum bias of GNNs.
- Graph Positional Autoencoders as Self-supervised Learners5
This work proposes Graph Positional Autoencoders (GraphPAE), which employs a dual-path architecture to reconstruct both node features and positions and achieves state-of-the-art performance and consistently outperforms the baselines by a large margin.
- Graph-GRPO: Training Graph Flow Models with Reinforcement Learning4
This paper proposes Graph-GRPO, an online reinforcement learning (RL) framework for training GFMs under verifiable rewards that achieves state-of-the-art performance on the molecular optimization tasks, outperforming graph-based and fragment-based RL methods as well as classic genetic algorithms.
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- Understanding Dataset Distillation via Spectral Filtering3
UniDD is introduced, a spectral filtering framework that unifies diverse DD objectives and reveals that the essence of DD fundamentally lies in matching frequency-specific features.
- Full-Spectrum Graph Neural Networks: Expressive and Scalable–
It is proved that FSpecGNNs can be at most as expressive as Local 2-GNN while universally approximating node-pair signals, and combined with a low-rank approximation that reduces full-spectrum convolution to a combination of polynomial spectral filters, it enables learning on large graphs.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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