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Found 38 papers, 17 papers with code
Distilling Reasoning Ability from Large Language Models with Adaptive Thinking
This mechanism enables SLM to analyze and think about complex questions, but it also makes answer correctness highly sensitive to minor errors in rationale.
One-Step Late Fusion Multi-view Clustering with Compressed Subspace
Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in the multi-view clustering (MVC) field, owing to its excellent computational speed and clustering performance.
Learn from View Correlation: An Anchor Enhancement Strategy for Multi-view Clustering
By learning from view correlation we enhance the anchors of the current view using the relationships between anchors and samples on neighboring views thereby narrowing the spatial distribution of anchors on similar views.
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views.
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.
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).
Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning
To this end, we propose the graph Structure Guided Multimodal Pretrained Transformer for knowledge graph reasoning, termed SGMPT.
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
It makes evaluating models for large-scale temporal graph clustering challenging.
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.
Message Intercommunication for Inductive Relation Reasoning
However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.
Deep Temporal Graph Clustering
To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs.
Ranked #1 on
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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.
RARE: Robust Masked Graph Autoencoder
To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space.
GANN: Graph Alignment Neural Network for Semi-Supervised Learning
Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning.
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations.
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world.
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.
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.
GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
During the training procedure, we notice the distinct optimization goals for training learnable augmentors and contrastive learning networks.
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.
Attributed Graph Clustering with Dual Redundancy Reduction
To this end, we develop a novel method termed attributed graph clustering with dual redundancy reduction (AGC-DRR) to reduce the information redundancy in both input space and latent feature space.
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.
Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph
Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations.
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.
Siamese Attribute-missing Graph Auto-encoder
First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit from more abundant and diverse information.
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
iii) The partition level information has not been utilized in existing work.
Multi-view Deep One-class Classification: A Systematic Exploration
Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC.
Deep Distribution-preserving Incomplete Clustering with Optimal Transport
Clustering is a fundamental task in the computer vision and machine learning community.
Localized Simple Multiple Kernel K-Means
After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix.
Deep Fusion Clustering Network
Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning.
Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views.
