24 citations · 66 across the 24 of their papers we have counts for
25 papers
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…
Truth Discovery in Sequence Labels from Crowds
Nasim Sabetpour, Adithya Kulkarni, Sihong Xie +1
Annotation quality and quantity positively affect the learning performance of sequence labeling, a vital task in Natural Language Processing. Hiring domain experts to annotate a co…
Robust Spammer Detection by Nash Reinforcement Learning
Yingtong Dou, Guixiang Ma, Philip S. Yu +1
Online reviews provide product evaluations for customers to make decisions. Unfortunately, the evaluations can be manipulated using fake reviews ("spams") by professional spammers,…
Rigorous Explanation of Inference on Probabilistic Graphical Models
Yifei Liu, Chao Chen, Xi Zhang +1
Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Sophistic…
Scalable Explanation of Inferences on Large Graphs
Chao Chen, Yifei Liu, Xi Zhang +1
Probabilistic inferences distill knowledge from graphs to aid human make important decisions. Due to the inherent uncertainty in the model and the complexity of the knowledge, it 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…
MARS: Memory Attention-Aware Recommender System
Lei Zheng, Chun-Ta Lu, Lifang He +4
In this paper, we study the problem of modeling users' diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent disti…
Product Function Need Recognition via Semi-supervised Attention Network
Hu Xu, Sihong Xie, Lei Shu +1
Functionality is of utmost importance to customers when they purchase products. However, it is unclear to customers whether a product can really satisfy their needs on functions. F…
Dual Attention Network for Product Compatibility and Function Satisfiability Analysis
Hu Xu, Sihong Xie, Lei Shu +1
Product compatibility and their functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products. Due to th…
IAD: Interaction-Aware Diffusion Framework in Social Networks
Xi Zhang, Yuan Su, Siyu Qu +3
In networks, multiple contagions, such as information and purchasing behaviors, may interact with each other as they spread simultaneously. However, most of the existing informatio…
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…
CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews
Hu Xu, Sihong Xie, Lei Shu +1
Product reviews contain a lot of useful information about product features and customer opinions. One important product feature is the complementary entity (products) that may pote…
Multi-source Hierarchical Prediction Consolidation
Chenwei Zhang, Sihong Xie, Yaliang Li +3
In big data applications such as healthcare data mining, due to privacy concerns, it is necessary to collect predictions from multiple information sources for the same instance, wi…
CENI: a Hybrid Framework for Efficiently Inferring Information Networks
Qingbo Hu, Sihong Xie, Shuyang Lin +2
Nowadays, the message diffusion links among users or websites drive the development of countless innovative applications. However, in reality, it is easier for us to observe the ti…
Future Influence Ranking of Scientific Literature
Senzhang Wang, Sihong Xie, Xiaoming Zhang +3
Researchers or students entering a emerging research area are particularly interested in what newly published papers will be most cited and which young researchers will become infl…