Arrow Research search

Author name cluster

Yezi Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers
1 author row

Possible papers

4

IJCAI Conference 2025 Conference Paper

DGExplainer: Explaining Dynamic Graph Neural Networks via Relevance Back-propagation

  • Yezi Liu
  • Jiaxuan Xie
  • Yanning Shen

Dynamic graph neural networks (dynamic GNNs) have demonstrated remarkable effectiveness in analyzing time-varying graph-structured data. However, their black-box nature often hinders users from understanding their predictions, which can limit their applications. In recent years, there has been a surge in research aimed at explaining GNNs, but most studies have focused on static graphs, leaving the explanation of dynamic GNNs relatively unexplored. Explaining dynamic GNNs presents a unique challenge due to their complex spatial and temporal structures. As a result, existing approaches designed for explaining static graphs are not directly applicable to dynamic graphs because they ignore temporal dependencies among graph snapshots. To address this issue, we propose DGExplainer, which offers a reliable explanation of dynamic GNN predictions. DGExplainer utilizes the relevance back-propagation technique both time-wise and layer-wise. Specifically, it incorporates temporal information by computing the relevance of node representations along the inverse of the time evolution. Additionally, for each time step, it calculates layer-wise relevance from a graph-based module by redistributing the relevance of node representations along the back-propagation path. Quantitative and qualitative experimental results on six real-world datasets demonstrate the effectiveness of DGExplainer in identifying important nodes for link prediction and node regression in dynamic GNNs.

PDF Details DOI

TMLR Journal 2025 Journal Article

Gradient Inversion Attack on Graph Neural Networks

  • Divya Anand Sinha
  • Yezi Liu
  • Ruijie Du
  • Athina Markopoulou
  • Yanning Shen

Graph federated learning is of essential importance for training over large graph datasets while protecting data privacy, where each client stores a subset of local graph data, while the server collects the local gradients and broadcasts only the aggregated gradients. Recent studies reveal that a malicious attacker can steal private image data from the gradient exchange of neural networks during federated learning. However, the vulnerability of graph data and graph neural networks under such attacks, i.e., reconstructing both node features and graph structure from gradients, remains largely underexplored. To answer this question, this paper studies the problem of whether private data can be reconstructed from leaked gradients in both node classification and graph classification tasks and proposes a novel attack named Graph Leakage from Gradients (GLG). Two widely used GNN frameworks are analyzed, namely GCN and GraphSAGE. The effects of different model settings on reconstruction are extensively discussed. Theoretical analysis and empirical validation demonstrate that, by leveraging the unique properties of graph data and GNNs, GLG achieves more accurate reconstruction of both nodal features and graph structure from gradients.

PDF Details

AAAI Conference 2022 System Paper

RES: An Interpretable Replicability Estimation System for Research Publications

  • Zhuoer Wang
  • Qizhang Feng
  • Mohinish Chatterjee
  • Xing Zhao
  • Yezi Liu
  • Yuening Li
  • Abhay Kumar Singh
  • Frank M. Shipman

Reliable and faithful research is the cornerstone of breakthrough advancements and disruptive innovations. Assessing the credibility of scientific findings and claims in research publications has long been a time-consuming and challenging task for researchers and decision-makers. In this paper, we introduce RES - an intelligent system that assists humans in analyzing the credibility of scientific findings and claims in research publications in the field of social and behavioral sciences by estimating their replicability. The pipeline of RES consists of four major modules that perform feature extraction, replicability estimation, result explanation, and sentiment analysis respectively. Our evaluation based on human experts’ assessments suggests that the RES has achieved adequate performance. The RES is also built with a Graphical User Interface (GUI) that is publicly accessible at https: //tamu-infolab. github. io/RES/.

PDF Details
v2026.09.13