116 citations · 225 across the 27 of their papers we have counts for
13 papers · 1 filter
Disentangled Generation with Information Bottleneck for Few-Shot Learning
Zhuohang Dang, Jihong Wang, Minnan Luo +3
Few-shot learning (FSL), which aims to classify unseen classes with few samples, is challenging due to data scarcity. Although various generative methods have been explored for FSL…
Generalized Category Discovery with Decoupled Prototypical Network
Wenbin An, Feng Tian, Qinghua Zheng +3
Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Wi…
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Shangbin Feng, Zhaoxuan Tan, Zilong Chen +5
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches ar…
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning
Wenbin An, Feng Tian, Ping Chen +3
Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of…
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach
Shujie Yang, Binchi Zhang, Shangbin Feng +4
Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and e…
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Xinshun Feng, Herun Wan, Shangbin Feng +4
Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem, which results in indistinguishable node representations and low model performance with more GNN layers.…
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
Zhenyu Lei, Herun Wan, Wenqian Zhang +5
Twitter bots are automatic programs operated by malicious actors to manipulate public opinion and spread misinformation. Research efforts have been made to automatically identify b…
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Zhaoxuan Tan, Zilong Chen, Shangbin Feng +4
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion. Most exi…
Towards Explanation for Unsupervised Graph-Level Representation Learning
Qinghua Zheng, Jihong Wang, Minnan Luo +4
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the inpu…
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Shangbin Feng, Zhaoxuan Tan, Herun Wan +19
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. St…
Noise-Tolerant Learning for Audio-Visual Action Recognition
Haochen Han, Qinghua Zheng, Minnan Luo +3
Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the mode…
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
Chengyou Jia, Minnan Luo, Caixia Yan +2
Recently, weakly supervised person search is proposed to discard human-annotated identities and train the model with only bounding box annotations. A natural way to solve this prob…
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Jihong Wang, Minnan Luo, Jundong Li +3
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rende…