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- Xiaofeng ZhuSuggested from co-authorship
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Works21 from public data
- Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity177
An imputation-free and fusion-free deep IMVC framework, an EM-like optimization strategy to alternately promote feature learning and clustering, and an implementation of the high-dimensional mapping as well as shows the mechanism to mine the multi-view cluster complementarity.
- Simple Unsupervised Graph Representation Learning175
The proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the inter-class variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchorembeddings for reducing the intra- class variation.
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- Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective15
This paper theoretically revisiting SHGL from the spectral clustering perspective and introducing a novel framework enhanced by rank and dual consistency constraints that integrates node-level and cluster-level consistency constraints that concurrently capture invariant and clustering information to facilitate learning in downstream tasks.
- Self-Training Based Few-Shot Node Classification by Knowledge Distillation13
A new self-training FSNC method by involving the representation distillation and the pseudo-label distillation, designed to conduct knowledge distillation on the pseudo-labels to improve their quality.
- Multiplex Graph Representation Learning via Common and Private Information Mining9
This paper proposes a new SMGRL method by jointly mining the common information and the private information in the multiplex graph while minimizing the redundant information within node representations.
- Simple Self-supervised Multiplex Graph Representation Learning9
The proposed method removes the processes (i.e., data augmentation and negative sample encoding) for the SMGRL and designs a simple pretext task, for achieving the efficiency and effectiveness.
- Multi-view Unsupervised Graph Representation Learning8
This paper designs a new multi-view unsupervised graph representation learning method including adaptive data augmentation and multi-View contrastive learning, to address some issues of Contrastive learning ignoring the information from feature space.
- Multiplex Graph Representation Learning with Homophily and Consistency6
This paper proposes to restructure the multi-order relationships of every graph between every node and its multi-order neighbors to improve the homophily and reduce the impact of the heterophily in the graph structure and theoretically proves the method to achieve class-level consistency.
- HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters4
A unified framework is proposed that combines two new adapters with potential labeled data extension to improve the generalization of pre-trained HGNN models and designs dual structure-aware adapters to adaptively fit task-related homogeneous and heterogeneous structural information.
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- Meta Label Correction with Generalization Regularizer1
This paper investigates a new gradient score method to filter noisy labels with less computation cost, and theoretically design a new generalization regularizer into the meta-learner and the base learner, for correcting noisy labels as well as achieving the generalization ability.
- MCD-CLIP: Multi-view Chest Disease Diagnosis with Disentangled CLIP1
MCD-CLIP, a CLIP-based multi-view chest disease diagnosis method, uses visual prompts and a Prompt-Aligner to align prompts across views, along with the additional text representation for efficient transfer, and employs Adapters to disentangle the image representation, maintaining consistency and complementarity from different views.
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- Bridging Feature-structural Homophily and Long-range Heterogeneity for Self-supervised Heterogeneous Graph Learning–
This work proposes a self-expressive solver that captures the complementary homophily between meta-paths and node features to obtain homophilous representations and designs separate path encoders to model diverse interactions, thus explicitly including cross-type interactions while mitigating noise via adaptive fusion.
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Publication data from OpenAlex; 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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