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Found 20 papers, 12 papers with code
Test-Time Training on Graphs with Large Language Models (LLMs)
Graph Neural Networks have demonstrated great success in various fields of multimedia.
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training
Remarkably, we show that even smaller base models, when equipped with our proposed approach, can achieve results comparable to those of larger models, illustrating the potential of our approach in harnessing the power of rationales for improved multimodal reasoning.
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views.
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.
Reinforcement Graph Clustering with Unknown Cluster Number
To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).
Dink-Net: Neural Clustering on Large Graphs
Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.
Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer
In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
Moreover, most of the existing methods ignore leveraging the beneficial information from aliasing relations (AR), i. e., data-rich relations with similar contextual semantics to the target data-poor relation.
Cluster-guided Contrastive Graph Clustering Network
Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views.
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.
Attribute Graph Clustering via Learnable Augmentation
To this end, we propose an Attribute Graph Clustering method via Learnable Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for high-quality and suitable augmented samples for CDGC.
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure
To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models.
Mixed Graph Contrastive Network for Semi-Supervised Node Classification
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years.
Simple Contrastive Graph Clustering
To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function.
Improved Dual Correlation Reduction Network
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task.
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning
Specifically, the proposed algorithm outperforms the second best algorithm (Comatch) with 5. 3% by achieving 88. 73% classification accuracy when only two labels are available for each class on the CIFAR-10 dataset.
Deep Graph Clustering via Dual Correlation Reduction
To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner.
