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Found 83 papers, 45 papers with code
Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph
Specifically, we design two augmentation techniques, i. e., positive semantics matching and negative semantics contrast, to provide more reference texts for each graph node or text description.
Knowledge-aware contrastive heterogeneous molecular graph learning
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design.
Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities
Due to their excellent drug-like and pharmacokinetic properties, small molecule drugs are widely used to treat various diseases, making them a critical component of drug discovery.
Can Molecular Evolution Mechanism Enhance Molecular Representation?
We extract and analyze the changes in the evolutionary pathway and explore combining it with existing molecular representations.
Hierarchical Superpixel Segmentation via Structural Information Theory
Superpixel segmentation is a foundation for many higher-level computer vision tasks, such as image segmentation, object recognition, and scene understanding.
Differential Evolution Integrated Hybrid Deep Learning Model for Object Detection in Pre-made Dishes
Object detection is a key technology for selecting ingredients and evaluating the quality of dishes in the pre-made dishes industry.
PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
To address these findings, this paper proposes a Periodic Dynamic Graph to Sequence Model (PDG2Seq) for traffic flow prediction.
Ranked #2 on
Traffic Prediction
on PeMSD3
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference
To achieve this, we introduce a hierarchical user preference modeling framework that organizes user representations by the neural network encoder's depth, allowing separate treatment of shallow and deeper subspaces.
DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts
Empirically, fewer layers are sufficient for message passing in smaller graphs, while larger graphs typically require deeper networks to capture long-range dependencies and global features.
Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model
TransDLM leverages standardized chemical nomenclature as semantic representations of molecules and implicitly embeds property requirements into textual descriptions, thereby preventing error propagation during diffusion process.
Fragment-Masked Diffusion for Molecular Optimization
Molecular optimization is a crucial aspect of drug discovery, aimed at refining molecular structures to enhance drug efficacy and minimize side effects, ultimately accelerating the overall drug development process.
Graph Neural Networks for Brain Graph Learning: A Survey
Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders.
Regressor-free Molecule Generation to Support Drug Response Prediction
As a result, we introduce regressor-free guidance molecule generation to ensure sampling within a more effective space and support DRP.
A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction
In the drug development engineering field, predicting novel drug-target interactions is extremely crucial. However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein information during DTI prediction.
Hi-GMAE: Hierarchical Graph Masked Autoencoders
To ensure masking uniformity of subgraphs across these scales, we propose a novel coarse-to-fine strategy that initiates masking at the coarsest scale and progressively back-projects the mask to the finer scales.
Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders
To this end, we introduce a novel structure-guided masking strategy (i. e., StructMAE), designed to refine the existing GMAE models.
Heterogeneous Subgraph Transformer for Fake News Detection
To bridge the gap, this work proposes a heterogeneous subgraph transformer (HeteroSGT) to exploit subgraphs in our constructed heterogeneous graph.
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation.
Ranked #1 on
Stereo Disparity Estimation
on Middlebury 2014
Single Stage Adaptive Multi-Attention Network for Image Restoration
In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring.
Ranked #2 on
Image Denoising
on DND
RGDAN: A random graph diffusion attention network for traffic prediction
RGDAN comprises a graph diffusion attention module and a temporal attention module.
Ranked #1 on
Traffic Prediction
on NE-BJ
Exploring Sparsity in Graph Transformers
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks.
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.
Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection
While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance.
Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model
Then, two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives.
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments.
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks
Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.
Heterogeneous Social Event Detection via Hyperbolic Graph Representations
This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space.
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
A Comprehensive Survey on Graph Summarization with Graph Neural Networks
Hence, this paper presents a comprehensive survey of progress in deep learning summarization techniques that rely on graph neural networks (GNNs).
A Comprehensive Survey on Automatic Knowledge Graph Construction
A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution.
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning.
State of the Art and Potentialities of Graph-level Learning
Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.
Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks
In this paper, we construct a dual-layer graph (i. e., the news layer and the user layer) to extract multiple relations of news and users in social networks to derive rich information for detecting fake news.
Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues.
A robust feature reinforcement framework for heterogeneous graphs neural networks
Heterogeneous graph embedding is a crucial step in HGNNs.
Towards a Better Model with Dual Transformer for Drug Response Prediction
For the branch of cell lines genomics, we use the multi-headed attention mechanism to globally represent the genomics sequence.
Ranked #1 on
Drug Response Prediction
on GDSCv2
DAGAD: Data Augmentation for Graph Anomaly Detection
To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation
To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology.
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
Automating DBSCAN via Deep Reinforcement Learning
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality.
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
Graph-level Neural Networks: Current Progress and Future Directions
To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.
Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures
Knowledge-aware methods have boosted a range of natural language processing applications over the last decades.
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.
Curvature Graph Generative Adversarial Networks
In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.
Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmap
In this study, we developed a single-stage rotating object detector via two points with a solar corona heatmap (ROTP) to detect oriented objects.
Graph Structure Learning with Variational Information Bottleneck
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.
DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control
Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.
Event Extraction by Associating Event Types and Argument Roles
It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.
Transferring Knowledge Distillation for Multilingual Social Event Detection
Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.
A Survey on Deep Learning Event Extraction: Approaches and Applications
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms
In the end, we built global graphs to abstract spatial interaction patterns and extract features for disease diagnosis.
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.
Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm
We present a computable stochastic approximation type algorithm, namely the stochastic linearized proximal method of multipliers, to solve this convex stochastic optimization problem.
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System
As the process should be socially and economically profitable, the task of vehicle dispatching is highly challenging, specially due to the time-varying travel demands and traffic conditions.
A Comprehensive Survey on Community Detection with Deep Learning
A community reveals the features and connections of its members that are different from those in other communities in a network.
Task-adaptive Neural Process for User Cold-Start Recommendation
In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP).
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Graph representation learning has attracted increasing research attention.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
Graph Stochastic Neural Networks for Semi-supervised Learning
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.
Graph Geometry Interaction Learning
To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.
A Deep Framework for Cross-Domain and Cross-System Recommendations
Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.
Opinion Maximization in Social Trust Networks
However, the practical significance of the existing studies on this subject is limited for two reasons.
Social and Information Networks Computer Science and Game Theory J.4
Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter
A meta-path based heterogeneous graph attention network framework is proposed to capture the global semantic relations of text contents, together with the global structure information of source tweet propagations for rumor detection.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
Deep Learning for Community Detection: Progress, Challenges and Opportunities
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.
Deep Active Learning for Anchor User Prediction
Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction.
Deep segmentation networks predict survival of non-small cell lung cancer
Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value.
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.
Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification
Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years.
Dynamic Island Model based on Spectral Clustering in Genetic Algorithm
One is that after a certain number of generations, different islands may retain quite similar, converged sub-populations thereby losing diversity and decreasing efficiency.
Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms
In this paper, a scheme of setting the success rate of migration based on subpopulation diversity at each interval is proposed.
Temporal Feature Selection on Networked Time Series
For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series.
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning
To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding
In this article, how word embeddings can be used as features in Chinese sentiment classification is presented.
