activity
20162020
most citedFederated Dynamic GNN with Secure Aggregation

24 citations · 72 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CR20209 cited

Data-Driven Network Intrusion Detection: A Taxonomy of Challenges and Methods

Dylan Chou, Meng Jiang

Data-driven methods have been widely used in network intrusion detection (NID) systems. However, there are currently a number of challenges derived from how the datasets are being…

cs.CR202024 cited

Federated Dynamic GNN with Secure Aggregation

Meng Jiang, Taeho Jung, Ryan Karl +1

Given video data from multiple personal devices or street cameras, can we exploit the structural and dynamic information to learn dynamic representation of objects for applications…

cs.LG20202 cited

Learning Attribute-Structure Co-Evolutions in Dynamic Graphs

Daheng Wang, Zhihan Zhang, Yihong Ma +4

Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the no…

cs.LG20203 cited

Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors

Daheng Wang, Meng Jiang, Munira Syed +4

User behavior modeling is important for industrial applications such as demographic attribute prediction, content recommendation, and target advertising. Existing methods represent…

cs.AI2020

Specification mining and automated task planning for autonomous robots based on a graph-based spatial temporal logic

Zhiyu Liu, Meng Jiang, Hai Lin

We aim to enable an autonomous robot to learn new skills from demo videos and use these newly learned skills to accomplish non-trivial high-level tasks. The goal of developing such…

cs.SI202013 cited

Improving Generalizability of Fake News Detection Methods using Propensity Score Matching

Bo Ni, Zhichun Guo, Jianing Li +1

Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this pa…