Three papers and one tutorial proposal from the KAIST Graduate School of AI got accepted to the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-20).
KDD is one of the top conferences in Data Mining. In 2020, it will take place at the San Diego Convention Center in San Diego, CA, from August 23 to August 27.
List of the papers:
“Incremental Lossless Graph Summarization”
Jihoon Ko* (KAIST AI), Yunbum Kook*, and Kijung Shin (KAIST AI)
“SSumM: Sparse Summarization of Massive Graphs”
Kyuhan Lee* (KAIST AI), Hyeonsoo Jo*(KAIST AI), Jihoon Ko (KAIST AI), Sungsu Lim, and Kijung Shin (KAIST AI)
“Structural Patterns and Generative Models of Real-world Hypergraphs”
Manh Tuan Do, Se-eun Yoon, Bryan Hooi Kuen-Yew, and Kijung Shin (KAIST AI)
“Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data” (Tutorial)
Jaesik Choi (KAIST AI)