Shirui Pan

Latest

• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
• Hyperspectral Image Classification with Context-aware Dynamic Graph Convolutional Networks
• A Relation-Specific Attention Network for Joint Entity and Relation Extraction
• One-Shot Neural Architecture Search via Novelty Driven Sampling
• Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning
• A comprehensive survey on graph neural networks
• Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications
• Unsupervised Domain Adaptive Graph Convolutional Networks
• Going Deep: Graph Convolutional Ladder-shape Networks
• GSSNN: Graph Smoothing Splines Neural Networks
• Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks
• Clustering Social Audiences in Business Information Networks
• Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning
• Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion
• An Effective and Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals
• Domain-Adversarial Graph Neural Networks for Text Classification
• Learning Graph Embedding With Adversarial Training Methods
• Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning
• Relation Structure-Aware Heterogeneous Graph Neural Network
• Exploiting Implicit Influence from Information Propagation for Social Recommendation
• Attributed Graph Clustering: A Deep Attentional Embedding Approach
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
• Influence Spread in Geo-Social Networks: A Multi-Objective Optimization Perspective
• Low-Bit Quantization for Attributed Network Representation Learning
• CFOND: consensus factorization for co-clustering networked data
• Cost-sensitive parallel learning framework for insurance intelligence operation
• Detecting Suicidal Ideation with Data Protection in Online Communities
• Identify topic relations in scientific literature using topic modeling
• IEEE access special section editorial: advanced data analytics for large-scale complex data environments
• Label Embedding with Partial Heterogeneous Contexts
• Measuring distance-based semantic similarity using meronymy and hyponymy relations
• Social recommendation with evolutionary opinion dynamics
• Time series feature learning with labeled and unlabeled data
• A hybrid user experience evaluation method for mobile games
• A three-layered mutually reinforced model for personalized citation recommendation
• Active discriminative network representation learning
• Advances in processing, mining, and learning complex data: From foundations to real-world applications
• Adversarially regularized graph autoencoder for graph embedding
• Binarized attributed network embedding
• Cost-sensitive hybrid neural networks for heterogeneous and imbalanced data
• Cross-domain deep learning approach for multiple financial market prediction
• DiSAN: directional self-attention network for RNN/CNN-free language understanding
• Discrete network embedding
• Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network
• FraudNE: a joint embedding approach for fraud detection
• Hashing for adaptive real-time graph stream classification with concept drifts
• Heterogeneous information network embedding based personalized query-focused astronomy reference paper recommendation
• Low-rank and sparse matrix factorization for scientific paper recommendation in heterogeneous network
• Multi-instance learning with discriminative bag mapping
• Multiple structure-view learning for graph classification
• Query-oriented citation recommendation based on network correlation
• Supervised learning for suicidal ideation detection in online user content
• Boosting for graph classification with universum
• Graph ladder networks for network classification
• MGAE: marginalized graph autoencoder for graph clustering
• Multi-document summarization based on sentence cluster using Non-negative Matrix Factorization
• Positive and unlabeled multi-graph learning
• Task sensitive feature exploration and learning for multitask graph classification
• Towards large-scale social networks with online diffusion provenance detection
• Universal network representation for heterogeneous information networks
• Classifying networked text data with positive and unlabeled examples
• Co-clustering enterprise social networks
• Direct discriminative bag mapping for multi-instance learning
• Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering
• Joint structure feature exploration and regularization for multi-task graph classification
• Multi-graph-view subgraph mining for graph classification
• SODE: Self-adaptive one-dependence estimators for classification
• Tri-party deep network representation
• Boosting for multi-graph classification
• CogBoost: boosting for fast cost-sensitive graph classification
• Finding the best not the most: regularized loss minimization subgraph selection for graph classification
• Graph ensemble boosting for imbalanced noisy graph stream classification
• Locally weighted learning: how and when does it work in Bayesian networks?
• Mining top-k minimal redundancy frequent patterns over uncertain databases
• Multi-graph-view learning for complicated object classification
• Multi-graph-view Learning for Graph Classification
• Self-adaptive attribute weighting for Naive Bayes classification
• Attribute weighting: how and when does it work for Bayesian Network Classification
• Dual instance and attribute weighting for Naive Bayes classification
• Exploring features for complicated objects: cross-view feature selection for multi-instance learning
• Multi-graph learning with positive and unlabeled bags
• Graph classification with imbalanced class distributions and noise
• Graph stream classification using labeled and unlabeled graphs
• CGStream: Continuous correlated graph query for data streams
• Continuous top-k query for graph streams
• Dynamic classifier ensemble for positive unlabeled text stream classification
• Top-k correlated subgraph query for data streams
• Classifier ensemble for uncertain data stream classification
• Ensemble of multiple descriptors for automatic image annotation