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Graph Neural Networks - Applications & Link to Graph Queries (Wintersemester 2021/2022)

Lecturer: Prof. Dr. Holger Giese (Systemanalyse und Modellierung) , Christian Medeiros Adriano (Systemanalyse und Modellierung) , Matthias Barkowsky (Systemanalyse und Modellierung)

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.-22.10.2021
  • Teaching Form: Project / Seminar
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English

Programs & Modules

Data Engineering MA
Digital Health MA
  • APAD-Concepts and Methods
  • APAD-Technologies and Tools
  • APAD-Specialization
IT-Systems Engineering MA

Description

Graph Neural Networks are a set of novel techniques aimed at enabling deep neural networks to work on heterogeneous graphs, e.g., social networks, knowledge graphs, recommender systems, gene regulatory networks, traffic networks, etc. These heterogeneous graphs became central components of complex software systems, which make predictions and interventions on the structure and behavior of the system and its context (e.g., users, environment). Take for instance, the mitigation of the spread of an event (accident, rumors, disease) through a network (traffic, social network). The heterogeneous network would model the current state of connections, their strength, and multiple properties in their nodes and edges. This complex data would be used to  predict the propagation paths and indicate the best set of nodes to intervene to mitigate the spread of rumors or disease and its effect on a system as a whole (traffic, population). Interventions can involve changes in the connections (isolate, circumvent, shortcut) and the attributes of the nodes (duplicate, reset, extend).

Note however, that neural networks algorithms were initially developed to solve problems in the domains of computer vision (3D and 2D) and natural language processing (1D). These domains present Euclidean spaces that allow neural networks to leverage on the regularities inherent to coordinate systems, vector space structures, or fixed distributions. Because these regularities are not guaranteed to be present in heterogeneous networks, new extensions were developed to these algorithms.

In this seminar we will study extensions to neural networks and apply them to heterogeneous graphs of real world problems. We will tackle these domain-specific problems in a principled way by solving the more generic graph-related problems of node classification, structure discovery, path identification, subgraph generation. 

Literature

Zhou, J. et al., 2018, Graph neural networks: A review of methods and applications.

 Xu, K., et al., 2018, How powerful are graph neural networks? 

Liu, Z., et al., 2020, Introduction to Graph Neural Networks, Synthesis Lectures on Artificial Intelligence and Machine Learning) 

Leng, S., et al., 2019, Reconstructing directional causal networks with random forest: Causality meeting machine learning. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(9), 093130

Ke, N. R., et al., 2019)Learning neural causal models from unknown interventions.

Y. Yu, et al., 2019, Dag-gnn: Dag structure learning with graph neural networks.

Z. Wu, et al., 2020, A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems.

Learning

The course is a project seminar, which has an introductory phase comprising initial short lectures. After that, the students will work in groups on jointly identified experiments applying specific solutions to given problems and finally prepare a presentation and write a report about their findings concerning the experiments.

There will be an introductory phase to present basic concepts for the theme, including the necessary foundations.

Examination

We will grade the group's experiments (60%), reports (30%), and presentations (10%). Participation in the project seminar during meetings and other groups' presentations in the form of questions and feedback will also be required.

Dates

If you are interested in this course, please contact christian.adriano(at)hpi.de.

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