623 citations · 1.2k across the 23 of their papers we have counts for
25 papers
Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneity
Qin Chen, Guojie Song
Heterogeneous graphs (HGs) are common in real-world scenarios and often exhibit heterophily. However, most existing studies focus on either heterogeneity or heterophily in isolatio…
On Structural Expressive Power of Graph Transformers
Wenhao Zhu, Tianyu Wen, Guojie Song +2
Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed. I…
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
Di Jin, Luzhi Wang, Yizhen Zheng +5
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations n…
Hierarchical Transformer for Scalable Graph Learning
Wenhao Zhu, Tianyu Wen, Guojie Song +2
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning…
LEReg: Empower Graph Neural Networks with Local Energy Regularization
Xiaojun Ma, Hanyue Chen, Guojie Song
Researches on analyzing graphs with Graph Neural Networks (GNNs) have been receiving more and more attention because of the great expressive power of graphs. GNNs map the adjacency…
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily
Xiaojun Ma, Qin Chen, Yuanyi Ren +2
Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream…
Stepping Back to SMILES Transformers for Fast Molecular Representation Inference
Wenhao Zhu, Ziyao Li, Lingsheng Cai +1
In the intersection of molecular science and deep learning, tasks like virtual screening have driven the need for a high-throughput molecular representation generator on large chem…
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
Shuwen Yang, Ziyao Li, Guojie Song +1
Molecular representation learning is the first yet vital step in combining deep learning and molecular science. To push the boundaries of molecular representation learning, we pres…
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
Zheng Fang, Qingqing Long, Guojie Song +1
Spatial-temporal forecasting has attracted tremendous attention in a wide range of applications, and traffic flow prediction is a canonical and typical example. The complex and lon…
HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Ziyao Li, Shuwen Yang, Guojie Song +1
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning. Whereas incorporating 3D geometry of molecules (i.e. conformations)…
Lorentzian Graph Convolutional Networks
Yiding Zhang, Xiao Wang, Chuan Shi +2
Graph convolutional networks (GCNs) have received considerable research attention recently. Most GCNs learn the node representations in Euclidean geometry, but that could have a hi…
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
Qingqing Long, Yilun Jin, Yi Wu +1
Graph neural networks (GNNs) have achieved tremendous success in graph mining. However, the inability of GNNs to model substructures in graphs remains a significant drawback. Speci…
Learning Node Representations from Noisy Graph Structures
Junshan Wang, Ziyao Li, Qingqing Long +3
Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks…
EPNE: Evolutionary Pattern Preserving Network Embedding
Junshan Wang, Yilun Jin, Guojie Song +1
Information networks are ubiquitous and are ideal for modeling relational data. Networks being sparse and irregular, network embedding algorithms have caught the attention of many…
Streaming Graph Neural Networks via Continual Learning
Junshan Wang, Guojie Song, Yi Wu +1
Graph neural networks (GNNs) have achieved strong performance in various applications. In the real world, network data is usually formed in a streaming fashion. The distributions o…
Graph Structural-topic Neural Network
Qingqing Long, Yilun Jin, Guojie Song +2
Graph Convolutional Networks (GCNs) achieved tremendous success by effectively gathering local features for nodes. However, commonly do GCNs focus more on node features but less on…
GraLSP: Graph Neural Networks with Local Structural Patterns
Yilun Jin, Guojie Song, Chuan Shi
It is not until recently that graph neural networks (GNNs) are adopted to perform graph representation learning, among which, those based on the aggregation of features within the…
Multi-Component Graph Convolutional Collaborative Filtering
Xiao Wang, Ruijia Wang, Chuan Shi +2
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort…
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Ziqiang Cheng, Yang Yang, Wei Wang +3
Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging pro…
DANE: Domain Adaptive Network Embedding
Yizhou Zhang, Guojie Song, Lun Du +2
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods…
Tag2Vec: Learning Tag Representations in Tag Networks
Junshan Wang, Zhicong Lu, Guojie Song +3
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks. In real networks, nodes may have multiple tags but existing methods ign…
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
Ziyao Li, Liang Zhang, Guojie Song
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities an…
SepNE: Bringing Separability to Network Embedding
Ziyao Li, Liang Zhang, Guojie Song
Many successful methods have been proposed for learning low dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable proce…
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
Zhongang Qi, Tianchun Wang, Guojie Song +4
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can prov…