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teaching:ss20:lab-ca

Lab Computational Analytics - Algorithms for learning on temporal graphs

Linked data arise in various domains, e.g., in chem- and bioinformatics, social network analysis and computer vision, and can be naturally represented as graphs. Therefore, machine learning and data analytics with graphs have become an active research area of increasing importance.

A prominent method used for data analytics on graphs, like, e.g., supervised graph classification with support vector machines, are graph kernels, which compute a similarity score between pairs of graphs. In the last fifteen years, a plethora of graph kernels has been published. An important class of graph kernels are Weisfeiler–Leman subtree kernels. They are based on the well-known color refinement algorithm for isomorphism testing. Our lab will explore, evaluate and extend various variants of the WL-kernels. Furthermore, we will take a look at connections to other research areas like deep learning, pebble games or linear programming.

We will design efficient exact and approximate algorithms and data structures for computational analytics problems. Our focus will be on data analytics and graph algorithms. The LAB also includes experimental evaluation and documentation of the implemented software.

In this lab we will focus on (spatio-)temporal graphs. Many real-world graphs are temporal, e.g., in a social network persons only interact at specific points in time. Additionally the graphs can be enriched with spatial information, i.e., the GPS location data of members of the network. The temporal information directs possible dissemination processes on the graph, such as the spread of rumors, fake news, or diseases. Most current state-of-the-art methods for supervised graph classification are designed mainly for static graphs and may not be able to capture temporal information. Therefore, our goal is to design and evaluate classification approaches for temporal graphs.

No prior knowledge of graph classification or kernels is required.

Keywords: Data analytics, Graph algorithms, Temporal graphs, Machine learning, Graph kernels, Deep learning, Combinatorial optimization

Dates and Organization

If you are interested in joining this lab, write an email to lutz.oettershagen [add] cs.uni-bonn.de.

The lab will be hold completely in digital manner. We will use a webconference system (Zoom or similar). Contact Lutz Oettershagen to get the invitation to the conference.

Attending the initial meeting on Wed. 22.04.20 16:00 o'clock is mandatory for joining the lab.

The weekly meetings will be Wednesday 16:00 o'clock.

In the first meeting we will give a short introduction to temporal graphs and graph classification with a focus on the Weisfeiler-Leman graph kernel. Furthermore, all organizational questions will be answered. Official registration of the students to the lab can be done in the following weeks. The exact date until when the students need to be registered will also be announced during the initial meeting.

Goal of the lab is to design new models for temporal graph classification based on existing conventional static graph kernels or graph neural networks. The models are implemented and evaluated on real-world data. We start off with a mini-seminar phase in which each participant presents a beforehand assigned paper or topic. A final presentation of the work and the results, as well as a written report is expected at the end of the lab.

The following plan will be updated regularly.

Week Date
17 22.04.2020 Initial meeting
18 29.04.2020 Due date for official registration, assignment of kick-off seminar topics
19 06.05.2020 Plan for the talks
20 13.05.2020 Kick-off seminar presentations
21 20.05.2020 Regular meeting, discussion of implementations, planning further steps
22 27.05.2020
23 03.06.2020
24 10.06.2020
25 17.06.2020
26 24.06.2020
27 01.07.2020
28 08.07.2020
29 15.07.2020 Final presentation and due date for written report

Literature

Gitlab

DozentInnen: Mutzel, Oettershagen

teaching/ss20/lab-ca.txt · Last modified: 2020/05/20 17:19 by lutz.oettershagen

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