9 citations · 12 across the 5 of their papers we have counts for
8 papers
Efficient Estimation of Shortest-Path Distance Distributions to Samples in Graphs
Alan Zhu, Jiaqi Ma, Qiaozhu Mei
As large graph datasets become increasingly common across many fields, sampling is often needed to reduce the graphs into manageable sizes. This procedure raises critical questions…
: A Library for Efficient Data Attribution
Junwei Deng, Ting-Wei Li, Shiyuan Zhang +7
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models. As training data plays an increasing…
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
Jiaqi Ma, Xingjian Zhang, Hezheng Fan +6
Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader…
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem
Jiaqi Ma, Junwei Deng, Qiaozhu Mei
Graph neural networks (GNNs) have attracted increasing interests. With broad deployments of GNNs in real-world applications, there is an urgent need for understanding the robustnes…
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks
Jiaqi Ma, Bo Chang, Xuefei Zhang +1
Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the \t…
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Jiaqi Ma, Xinyang Yi, Weijing Tang +4
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint par…