22 citations · 75 across the 18 of their papers we have counts for
18 papers
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…
Line Graph Contrastive Learning for Link Prediction
Zehua Zhang, Shilin Sun, Guixiang Ma +1
Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and pre…
Memory-Augmented Graph Neural Networks: A Brain-Inspired Review
Guixiang Ma, Vy A. Vo, Theodore Willke +1
We provide a comprehensive review of the existing literature on memory-augmented GNNs. We review these works through the lens of psychology and neuroscience, which has several esta…
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Haoteng Tang, Guixiang Ma, Lei Guo +3
Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional network…
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning
Yao Xiao, Guixiang Ma, Nesreen K. Ahmed +4
To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (P…
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…
PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images
Haozhe Jia, Haoteng Tang, Guixiang Ma +4
Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of CO…
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma +6
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It…
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
Haoteng Tang, Guixiang Ma, Lifang He +2
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for gr…
A Vertex Cut based Framework for Load Balancing and Parallelism Optimization in Multi-core Systems
Guixiang Ma, Yao Xiao, Theodore L. Willke +3
High-level applications, such as machine learning, are evolving from simple models based on multilayer perceptrons for simple image recognition to much deeper and more complex neur…
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,…
Adversarial Attack on Hierarchical Graph Pooling Neural Networks
Haoteng Tang, Guixiang Ma, Yurong Chen +4
Recent years have witnessed the emergence and development of graph neural networks (GNNs), which have been shown as a powerful approach for graph representation learning in many ta…
Deep Graph Similarity Learning: A Survey
Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke +1
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, su…
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Jiahao Liu, Guixiang Ma, Fei Jiang +3
Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for cli…
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…
Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis
Guixiang Ma, Nesreen K. Ahmed, Ted Willke +4
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic dist…
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Guixiang Ma, Chun-Ta Lu, Lifang He +2
Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common n…