19 citations · 45 across the 8 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 9 cited
Learning Deep Graph Representations via Convolutional Neural Networks
Wei Ye, Omid Askarisichani, Alex Jones +1
Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. R-convolution graph kernels are pos…
cs.LG2020★ 4 cited
Incorporating User's Preference into Attributed Graph Clustering
Wei Ye, Dominik Mautz, Christian Boehm +2
Graph clustering has been studied extensively on both plain graphs and attributed graphs. However, all these methods need to partition the whole graph to find cluster structures. S…
cs.LG2020★ 19 cited
Tree++: Truncated Tree Based Graph Kernels
Wei Ye, Zhen Wang, Rachel Redberg +1
Graph-structured data arise ubiquitously in many application domains. A fundamental problem is to quantify their similarities. Graph kernels are often used for this purpose, which…