Fall 2022

Data Science Seminar: Graph Processing

Lecturers: Mourad Khayati, Alberto Lerner, and Rana Hussein 

Teaching language: English

Level: MSc students

Academic year: Fall 2022

Overview

Structure

Evaluation and Expectations

Schedule

List of Papers


Overview

The seminar on data science involves presentations that cover recent topics on data science. The area of this year’s seminar is graph processing.  In the scope of this seminar, we investigate papers that describe algorithms and operators to process graphs. The papers explore mechanisms that highlight the strengths and weaknesses of these methods when applied to graphs.


Structure

The goal for the students is to learn how to critically read and study research papers, describe a paper in a report, and present it in a seminar. Under supervision, students will select one paper to study and compare it with related work. This seminar aims to help students gather in-depth knowledge of an advanced topic and develop the skills required to describe a complex problem from the time series field in the form of both a presentation, a written report, and an empirical evaluation.

IMPORTANT NOTE: The papers will be distributed on a first come first serve basis.


Evaluation and Expectations

The final grade depends on the quality of the report, presentation, reproducibility experiments, and active participation during the seminar. Each participant prepares a self-contained report of min 6 pages and gives a presentation of 30 minutes. The report should describe in detail the proposed technique(s). The report might contain a small running example, counterexample(s) if any, and should explore the extreme cases where the proposed approach would perform best and worst. The reproducibility consists of reproducing the same set of experiments introduced in the paper using a different setup (dataset, metric, parameters, etc.).

Advice on how to:

IMPORTANT NOTE: Attendance is mandatory for the two-class seminar sessions. The total number of participants will be limited to 10.


Schedule

Kickoff Meeting. Date: Tue, 27.09.2022, 14:15-16:00, room: A 403

Setup and organization of the seminar, and paper assignment

----------------------------------------------------------------------

Date: Tue, 08.11.2022
Report deadline
Batch1

Date: Tue, 15.11.2022, all day, room: C433 or C411

Office meeting with students from Batch1

First Seminar Session. Date: Tue, 22.11.2022, 14:15-18:00, room: TBD

Presentations of Batch1

----------------------------------------------------------------------

Date: Tue, 29.11.2022
Report deadline of
Batch2

Date: Tue, 06.12.2022, all day, room: C433 or C411
Office meeting with students from
Batch2

Second Seminar Session. Date: Tue, 13.12.2022, 14:15-18:00, room: TBD

Presentations of Batch2

----------------------------------------------------------------------

Date: Tue, 10.01.2023
Deadline final Report of Batch1 and Batch2


Paper Assignment

The papers will be distributed on a first come first serve basis. To select one paper from the list of papers, please use the following link.

Paper & code

Presentation Date

Presenter

Mentor

Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series, PVLDB’21.

Code: https://helios2.mi.parisdescartes.fr/~themisp/series2graph

Mourad Khayati

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. KDD’20.

Code: https://github.com/nnzhan/MTGNN

Mourad Khayati

GraphTempo: An aggregation framework for evolving graphs, EDBT’22.

Code: https://github.com/etsoukanara/GraphTempo

Mourad Khayati

Forecasting Interaction Order on Temporal Graphs. KDD’21.

Code: https://github.com/xiawenwen49/TAT-code

Mourad Khayati

On Compressing Temporal Graphs, ICDE’22.

Code:  https://github.com/panagiotisl/evolving-graph-compression

Mourad Khayati

Pensieve: Skewness-Aware Version Switching for Efficient Graph Processing, SIGMOD’20.

Code: https://githubcom/CGCL-codes/Pensieve

Rana Hussein

Teseo and the Analysis of Structural Dynamic Graphs, PVLDB’21.

Code: https://github.com/cwida/teseo

Alberto Lerner

Time Constrained Continuous Subgraph Search over Streaming Graphs. ICDE’19.

Code: https://github.com/pkumod/timingsubg

Alberto Lerner

Sortledton: a Universal, Transactional Graph Data Structure. PVLDB’22.

Code: https://github.com/PerFuchs/gfe_driver

Alberto Lerner

DyGraph: A Dynamic Graph Generator and Benchmark Suite. GRADES @ SIGMOD’22.

Code: https://adacenter.org/dygraph

Alberto Lerner