53 citations · 275 across the 31 of their papers we have counts for
17 papers · 1 filter
Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
Qingyun Sun, Jianxin Li, Beining Yang +3
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption ca…
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-base…
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
Li Sun, Junda Ye, Hao Peng +2
Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior…
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer
Ziwei Fan, Zhiwei Liu, Chen Wang +3
Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the 'was interacted before' item-item…
DAGAD: Data Augmentation for Graph Anomaly Detection
Fanzhen Liu, Xiaoxiao Ma, Jia Wu +7
Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Rec…
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation
Yuecen Wei, Xingcheng Fu, Qingyun Sun +4
Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspe…
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Jiaqian Ren, Lei Jiang, Hao Peng +6
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link p…
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning
Li Sun, Junda Ye, Hao Peng +1
Representation learning on temporal graphs has drawn considerable research attention owing to its fundamental importance in a wide spectrum of real-world applications. Though a num…
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Qingyun Sun, Jianxin Li, Haonan Yuan +5
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topolog…
From Known to Unknown: Quality-aware Self-improving Graph Neural Network for Open Set Social Event Detection
Jiaqian Ren, Lei Jiang, Hao Peng +4
State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in social event detection tasks when restricted to a closed set of events. However, considering the l…
Automating DBSCAN via Deep Reinforcement Learning
Ruitong Zhang, Hao Peng, Yingtong Dou +4
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality. However, due to its high sensitivity parameters, the accuracy of the clu…
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao +12
Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years…
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
Jiaqian Ren, Lei Jiang, Hao Peng +3
The rising popularity of online social network services has attracted lots of research on mining social media data, especially on mining social events. Social event detection, due…
PyGOD: A Python Library for Graph Outlier Detection
Kay Liu, Yingtong Dou, Xueying Ding +5
PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based meth…
Curvature Graph Generative Adversarial Networks
Jianxin Li, Xingcheng Fu, Qingyun Sun +4
Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. However, for non-Euclidean graph data, the existing GAN-based graph represent…
Deep reinforcement learning guided graph neural networks for brain network analysis
Xusheng Zhao, Jia Wu, Hao Peng +9
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 co…
Sequential Recommendation via Stochastic Self-Attention
Ziwei Fan, Zhiwei Liu, Alice Wang +4
Sequential recommendation models the dynamics of a user's previous behaviors in order to forecast the next item, and has drawn a lot of attention. Transformer-based approaches, whi…