
Beyond Lowpass Filtering: Graph Convolutional Networks with Automatic Filtering
Graph convolutional networks are becoming indispensable for deep learnin...
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Differentiable Architecture Search Without Training Nor Labels: A Pruning Perspective
With leveraging the weightsharing and continuous relaxation to enable g...
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Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention du...
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SemiRiemannian Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are typically studied through the le...
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MultiScale Contrastive Siamese Networks for SelfSupervised Graph Representation Learning
Graph representation learning plays a vital role in processing graphstr...
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Learning Graph Neural Networks with Positive and Unlabeled Nodes
Graph neural networks (GNNs) are important tools for transductive learni...
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Taskadaptive Neural Process for User ColdStart Recommendation
User coldstart recommendation is a longstanding challenge for recommen...
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Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Federated learning is a new learning paradigm that decouples data collec...
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A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Community detection, a fundamental task for network analysis, aims to pa...
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Cyclic Label Propagation for Graph Semisupervised Learning
Graph neural networks (GNNs) have emerged as effective approaches for gr...
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Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization
Graph neural networks (GNNs) have been widely used to analyze the graph...
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Graph Geometry Interaction Learning
While numerous approaches have been developed to embed graphs into eithe...
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SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline
The Scholarly Document Processing (SDP) workshop is to encourage more ef...
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Medical Code Assignment with Gated Convolution and NoteCode Interaction
Medical code assignment from clinical text is a fundamental task in clin...
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MultiLevel Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification
Nowadays, deep learning methods, especially the Graph Convolutional Netw...
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Contrastive and Generative Graph Convolutional Networks for Graphbased SemiSupervised Learning
Graphbased SemiSupervised Learning (SSL) aims to transfer the labels o...
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Multivariate Relations Aggregation Learning in Social Networks
Multivariate relations are general in various types of networks, such as...
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attra...
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A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge ...
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Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Suicide is a critical issue in the modern society. Early detection and p...
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Hyperspectral Image Classification With ContextAware Dynamic Graph Convolutional Network
In hyperspectral image (HSI) classification, spatial context has demonst...
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Efficient NoveltyDriven Neural Architecture Search
OneShot Neural architecture search (NAS) attracts broad attention recen...
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Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or gr...
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Graph WaveNet for Deep SpatialTemporal Graph Modeling
Spatialtemporal graph modeling is an important task to analyze the spat...
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Decentralized Learning with Average Difference Aggregation for Proactive Online Social Care
The Internet and the Web are being increasingly used in proactive social...
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DAGCN: Dual Attention Graph Convolutional Networks
Graph convolutional networks (GCNs) have recently become one of the most...
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Learning Graph Embedding with Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate subs...
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A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent y...
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Learning Private Neural Language Modeling with Attentive Aggregation
Mobile keyboard suggestion is typically regarded as a wordlevel languag...
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Adversarially Regularized Graph Autoencoder
Graph embedding is an effective method to represent graph data in a low ...
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DiSAN: Directional SelfAttention Network for RNN/CNNFree Language Understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are wide...
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Iterative Views Agreement: An Iterative LowRank based Structured Optimization Method to MultiView Spectral Clustering
Multiview spectral clustering, which aims at yielding an agreement or c...
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Shirui Pan
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