44 citations · 48 across the 5 of their papers we have counts for
5 papers
Model-Agnostic Augmentation for Accurate Graph Classification
Jaemin Yoo, Sooyeon Shim, U Kang
Given a graph dataset, how can we augment it for accurate graph classification? Graph augmentation is an essential strategy to improve the performance of graph-based tasks, and has…
FURL: Fixed-memory and Uncertainty Reducing Local Triangle Counting for Graph Streams
Minsoo Jung, Sunmin Lee, Yongsub Lim +1
How can we accurately estimate local triangles for all nodes in simple and multigraph streams? Local triangle counting in a graph stream is one of the most fundamental tasks in gra…
M-Flash: Fast Billion-scale Graph Computation Using a Bimodal Block Processing Model
Hugo Gualdron, Robson Cordeiro, Jose Rodrigues-Jr +3
Recent graph computation approaches have demonstrated that a single PC can perform efficiently on billion-scale graphs. While these approaches achieve scalability by optimizing I/O…
Distributed Methods for High-dimensional and Large-scale Tensor Factorization
Kijung Shin, U. Kang
Given a high-dimensional large-scale tensor, how can we decompose it into latent factors? Can we process it on commodity computers with limited memory? These questions are closely…
VoG: Summarizing and Understanding Large Graphs
Danai Koutra, U Kang, Jilles Vreeken +1
How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the "importance" of a set of discovered subgraphs in a large graph? These are ex…