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Works465 from public data
- Graph WaveNet for Deep Spatial-Temporal Graph Modeling3,648
This paper proposes a novel graph neural network architecture, Graph WaveNet, for spatial-temporal graph modeling by developing a novel adaptive dependency matrix and learn it through node embedding, which can precisely capture the hidden spatial dependency in the data.
- A Survey on Knowledge Graphs: Representation, Acquisition, and Applications3,000
A comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research.
- Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks2,376
This paper proposes a general graph neural network framework designed specifically for multivariate time series data that outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.
- Unifying Large Language Models and Knowledge Graphs: A Roadmap1,833
A forward-looking roadmap for the unification of LLMs and KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge.
- Adversarially Regularized Graph Autoencoder for Graph Embedding902
A novel adversarial graph embedding framework for graph data that encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure.
- DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding794
A novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise) and a light-weight neural net is proposed, based solely on the proposed attention without any RNN/CNN structure, which outperforms complicated RNN models on both prediction quality and time efficiency.
- Deep Learning for Time Series Anomaly Detection: A Survey791
This survey provides a structured and comprehensive overview of state-of-the-art deep learning for time series anomaly detection and provides a taxonomy based on anomaly detection strategies and deep learning models.
- Graph Self-Supervised Learning: A Survey786
A unified framework is constructed that mathematically formalizes the paradigm of graph SSL and describes the applications of graphSSL across various research fields and summarize the commonly used datasets, evaluation benchmark, performance comparison and open-source codes ofgraph SSL.
- Attributed Graph Clustering: A Deep Attentional Embedding Approach669
This paper proposes a goal-directed deep learning approach to graph clustering, Deep Attentional Embedded Graph Clustering (DAEGC), which focuses on attributed graphs to sufficiently explore the two sides of information in graphs.
- Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning582
This paper proposes a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning and presents a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans.
- A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection578
This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.
- Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning537
This article presents a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation), which fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.
- MGAE: Marginalized Graph Autoencoder for Graph Clustering537
A marginalized graph convolutional network is proposed to corrupt network node content, allowing node content to interact with network features, and marginalizes the corrupted features in a graph autoencoder context to learn graph feature representations.
- Adversarially Regularized Graph Autoencoder.504
A novel adversarial graph embedding framework for graph data that encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure.
- Foundation Models for Time Series Analysis: A Tutorial and Survey497
This survey serves to consolidate the latest advancements in FMs pertinent to time series analysis, accentuating their theoretical underpinnings, recent strides in development, and avenues for future exploration.
- Tri-party deep network representation439
TriDNR is a tri-party deep network representation model, using information from three parties: node structure, node content, and node labels (if available) to jointly learn optimal node representation, which results in up to 79% classification accuracy gain, compared to state-of-the-art methods.
- A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning396
A unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection is developed and a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning is introduced.
- Learning Graph Embedding With Adversarial Training Methods375
This article presents a novel adversarially regularized framework for graph embedding, employing the graph convolutional network as an encoder, that embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph.
- Graph Neural Networks for Graphs With Heterophily: A Survey343
A systematic taxonomy that governs existing heterophilic GNN models for heterophilic graphs is proposed, aiming to facilitate the development of more effective GNNs across a spectrum of practical applications and learning tasks in the graph research community.
- Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects304
This article comprehensively review existing surveys related to SSL and time series, and provides a new taxonomy of existing time series SSL methods by summarizing them from three perspectives: generative- based, contrastive-based, and adversarial-based.
- Towards Unsupervised Deep Graph Structure Learning299
A novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning is proposed, where the learned graph topology is optimized by data itself without any external guidance.
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- Unsupervised Domain Adaptive Graph Convolutional Networks241
A novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs, which jointly exploits local and global consistency for feature aggregation and facilitates knowledge transfer between graphs.
- Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network231
A new HSI classification method based on the recently proposed Graph Convolutional Network (GCN), as it can flexibly encode the relations among arbitrarily structured non-Euclidean data, which yields significant improvement in the classification performance when compared with some state of theart approaches.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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