Search Results for author: Tian Bian

Found 8 papers, 3 papers with code

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

no code implementations18 Oct 2023 Qichao Wang, Tian Bian, Yian Yin, Tingyang Xu, Hong Cheng, Helen M. Meng, Zibin Zheng, Liang Chen, Bingzhe Wu

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products.

Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

1 code implementation4 Mar 2023 Tian Bian, Yuli Jiang, Jia Li, Tingyang Xu, Yu Rong, Yi Su, Timothy Kwok, Helen Meng, Hong Cheng

Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death.

counterfactual Representation Learning

Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification

no code implementations13 Aug 2022 Xin Wang, Heng Chang, Beini Xie, Tian Bian, Shiji Zhou, Daixin Wang, Zhiqiang Zhang, Wenwu Zhu

Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications.

Graph Classification

Weakly Supervised Graph Clustering

no code implementations29 Sep 2021 Tian Bian, Tingyang Xu, Yu Rong, Wenbing Huang, Xi Xiao, Peilin Zhao, Junzhou Huang, Hong Cheng

Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.

Clustering Graph Clustering +1

PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels

no code implementations29 Sep 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

Semi-supervised node classification on graphs is a fundamental problem in graph mining that uses a small set of labeled nodes and many unlabeled nodes for training, so that its performance is quite sensitive to the quality of the node labels.

Graph Mining Graph Neural Network +1

Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions

1 code implementation14 Jun 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang

This paper bridges the gap by proposing a pairwise framework for noisy node classification on graphs, which relies on the PI as a primary learning proxy in addition to the pointwise learning from the noisy node class labels.

Contrastive Learning Graph Learning +2

Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?

no code implementations12 Jul 2020 Tian Bian, Xi Xiao, Tingyang Xu, Yu Rong, Wenbing Huang, Peilin Zhao, Junzhou Huang

Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.

Clustering Community Detection +3

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

2 code implementations17 Jan 2020 Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang

Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.

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