48 citations · 103 across the 15 of their papers we have counts for
4 papers · 1 filter
SSSNET: Semi-Supervised Signed Network Clustering
Yixuan He, Gesine Reinert, Songchao Wang +1
Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited. In particular, mo…
DIGRAC: Digraph Clustering Based on Flow Imbalance
Yixuan He, Gesine Reinert, Mihai Cucuringu
Node clustering is a powerful tool in the analysis of networks. We introduce a graph neural network framework, named DIGRAC, to obtain node embeddings for directed networks in a se…
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Benedek Rozemberczki, Paul Scherer, Yixuan He +8
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of…
MagNet: A Neural Network for Directed Graphs
Xitong Zhang, Yixuan He, Nathan Brugnone +2
The prevalence of graph-based data has spurred the rapid development of graph neural networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets natural…