15 citations · 37 across the 8 of their papers we have counts for
8 papers
Vertex misalignment and changepoint localization in network time series
Tianyi Chen, Mohammad Sharifi Kiasari, Sijing Yu +5
Inference for time series of networks often relies on accurate vertex correspondence between network realizations at different times. In practice, such vertex alignments can be mis…
Parallel Motif-Based Community Detection
Tianyi Chen, Charalampos E. Tsourakakis
Community detection is a central task in graph analytics. Given the substantial growth in graph size, scalability in community detection continues to be an unresolved challenge. Re…
Q-DISCO: Query-Centric Densest Subgraphs in Networks with Opinion Information
Tianyi Chen, Atsushi Miyauchi, Charalampos E. Tsourakakis
Given a network , where each node is associated with a vector representing its opinion about different topics, how can we uncov…
Densest Diverse Subgraphs: How to Plan a Successful Cocktail Party with Diversity
Atsushi Miyauchi, Tianyi Chen, Konstantinos Sotiropoulos +1
Dense subgraph discovery methods are routinely used in a variety of applications including the identification of a team of skilled individuals for collaboration from a social netwo…
AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks
Tianyi Chen, Charalampos E. Tsourakakis
Benford's law describes the distribution of the first digit of numbers appearing in a wide variety of numerical data, including tax records, and election outcomes, and has been use…
Dense and well-connected subgraph detection in dual networks
Tianyi Chen, Francesco Bonchi, David Garcia-Soriano +2
Dense subgraph discovery is a fundamental problem in graph mining with a wide range of applications \cite{gionis2015dense}. Despite a large number of applications ranging from comp…
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu +2
Recent studies show that private training data can be leaked through the gradients sharing mechanism deployed in distributed machine learning systems, such as federated learning (F…
A Simple Spectral Failure Mode for Graph Convolutional Networks
Carey E. Priebe, Cencheng Shen, Ningyuan Huang +1
Neural networks have achieved remarkable successes in machine learning tasks. This has recently been extended to graph learning using neural networks. However, there is limited the…