24 citations · 66 across the 24 of their papers we have counts for
12 papers · 1 filter
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Yazheng Liu, Xi Zhang, Sihong Xie
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital…
Robust Ranking Explanations
Chao Chen, Chenghua Guo, Guixiang Ma +3
Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few…
Provable Robust Saliency-based Explanations
Chao Chen, Chenghua Guo, Rufeng Chen +5
To foster trust in machine learning models, explanations must be faithful and stable for consistent insights. Existing relevant works rely on the distance for stability as…
Efficient first-order predictor-corrector multiple objective optimization for fair misinformation detection
Eric Enouen, Katja Mathesius, Sean Wang +2
Multiple-objective optimization (MOO) aims to simultaneously optimize multiple conflicting objectives and has found important applications in machine learning, such as minimizing c…
Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection
Jiaxin Liu, Yuefei Lyu, Xi Zhang +1
User-generated reviews of products are vital assets of online commerce, such as Amazon and Yelp, while fake reviews are prevalent to mislead customers. GNN is the state-of-the-art…
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection
Yuefei Lyu, Xiaoyu Yang, Jiaxin Liu +3
Social networks are frequently polluted by rumors, which can be detected by advanced models such as graph neural networks. However, the models are vulnerable to attacks and underst…
Multi-objective Explanations of GNN Predictions
Yifei Liu, Chao Chen, Yazheng Liu +2
Graph Neural Network (GNN) has achieved state-of-the-art performance in various high-stake prediction tasks, but multiple layers of aggregations on graphs with irregular structures…
Explaining GNN over Evolving Graphs using Information Flow
Yazheng Liu, Xi Zhang, Sihong Xie
Graphs are ubiquitous in many applications, such as social networks, knowledge graphs, smart grids, etc.. Graph neural networks (GNN) are the current state-of-the-art for these app…
Self-learn to Explain Siamese Networks Robustly
Chao Chen, Yifan Shen, Guixiang Ma +4
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and i…
Semi-supervised Deep Representation Learning for Multi-View Problems
Vahid Noroozi, Sara Bahaadini, Lei Zheng +3
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to…
Securing Behavior-based Opinion Spam Detection
Shuaijun Ge, Guixiang Ma, Sihong Xie +1
Reviews spams are prevalent in e-commerce to manipulate product ranking and customers decisions maliciously. While spams generated based on simple spamming strategy can be detected…
SEVEN: Deep Semi-supervised Verification Networks
Vahid Noroozi, Lei Zheng, Sara Bahaadini +2
Verification determines whether two samples belong to the same class or not, and has important applications such as face and fingerprint verification, where thousands or millions o…