Master Seminar "Deep Learning for Graphs" / "Recent Developments in Data Science" (WS 2019/20)

News

Organisation

Contact: Prof. Dr. Thomas Seidl, Max Berrendorf, Evgeniy Faerman
Required: Successful participation in the lectures "Machine Learning" or "Deep Learning and Artificial Intelligence" or equivalent
Please indicate in the central registration form in uni2work in which semesters you had attended these courses.
Audience: The lecture is directed towards highly motivated Master students in Mediainformatics, Bioinformatics and Informatics as well as Data Science with strong interest in cutting edge research
Registration: via uni2work (from Aug 26)

Content

The availability of large amounts of data and increased computational power enabled the renaissance of neural networks as an universal paradigm for machine learning. While initially mainly boosted by advances on image classification, Deep Learning methods recently progress into different domains. One domain of particular interest are graphs, as they allow encoding less regular structures prevalent in numerous application domains, reaching from sensor networks to knowledge graphs.

In this seminar, students will discuss fundamental works in the quickly evolving area of deep learning for graphs, and in particular knowledge graphs, and will venture into the state-of-the-art of this exciting research area.

Schedule

Date	Time	Location	Description
We, 16.10.2019	14:15-15:45	131 (Oe 67)	Kickoff Slides
We, 11.12.2019	14:15-15:45	131 (Oe 67)	Group Presentations I Knowledge Graph Embeddings Homophily-based Embeddings on Non-attributed Graphs
We, 18.12.2019	14:15-15:45	131 (Oe 67)	Group Presentations II Graph Neural Networks Entity Alignment / Graph Alignment Recommender Systems
We, 15.01.2020	14:15-15:45	131 (Oe 67)	Individual Presentations I Structural-RNN: Deep learning on spatio-temporal graphs A. Jain, A. R. Zamir, S. Savarese, A. Saxena CVPR'16 Graph Representation Learning via Hard and Channel-Wise Attention Networks Hongyang Gao, Shuiwang Ji KDD'19 Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, Shaoyao He AAAI'19 How powerful are graph neural networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka ICLR'19 Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. Alessandro Epasto, Bryan Perozzi WWW'19
We, 22.01.2020	14:15-15:45	131 (Oe 67)	Individual Presentations II Iterative Deep Graph Learning for Graph Neural Networks Yu Chen, Lingfei Wu, Mohammed J. Zaki ICLR'20 (reject) Uncertainty-Aware Prediction for Graph Neural Networks Xujiang Zhao, Feng Chen, Shu Hu, jin-Hee Cho ICLR'20 (reject) NetGAN: Generating Graphs via Random Walks Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann ICML'18 Position-aware Graph Neural Networks Jiaxuan You, Rex Ying, Jure Leskovec ICML'19 Embedding Logical Queries on Knowledge Graphs William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec NIPS'18
We, 29.01.2020	14:15-15:45	131 (Oe 67)	Individual Presentations III Entity Alignment between Knowledge Graphs Using Attribute Embeddings Bayu Distiawan Trisedy, Jianzhong Qi, Rui Zhang AAAI'19 Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks Srijan Kumar, Xikun Zhang, Jure Leskovec KDD'19 Graph U-Nets Hongyang Gao, Shuiwang Ji ICML'19 Simplifying Graph Convolutional Networks Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger ICML'19 GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec NIPS'19
Su, 01.03.2020			Seminar Thesis Submission Deadline