HNR lunch lecture Mehwish Alam (November 18): Graph Representation Learning

Dear all,

From January 2021 onwards, monthly online lectures will shed a spotlight on recent research and ideas from the field of historical network analysis to promote discussion among the HNR community.

For November 18, our speaker will be Mehwish Alam, a Post-Doctoral Researcher/Senior Researcher at FIZ Karlsruhe – Leibniz Institute for Information Infrastructure and Karlsruhe Institute of Technology (KIT), Institute of Applied Informatics and Formal Description Methods (AIFB). Dr. Mehwish Alam will be speaking on “Graph Representation Learning” (see abstract below).

The lecture will start at 12:00 pm CET and ends one hour later. If you would like to participate, please register via Eventbrite before November 16, 2021. You will receive a Zoom link by email prior to the lunch lecture. After this talk, we will move towards a bimonthly rhythm, so watch out for the next lecture in January 2022!

Graph Representation Learning

This talk focuses on ways to perform representation learning on graphs such as citation network, author collaboration network, etc. It then moves on to the graphs with relational information, known as Knowledge Graphs. As they are typically used in an open-world setting, Knowledge Graphs can almost never assumed to be complete, i.e., some information will typically be missing. In order to address this problem, different Knowledge Graph embedding models have been proposed for automated Knowledge Graph completion. These models are mostly based on the tasks such as link prediction, triple classification, and entity classification/typing. This talk will also target the topic of Knowledge Graph embedding techniques. Finally, various applications of Knowledge Graphs and Knowledge Graph embeddings will be discussed.

We hope to welcome you online on November 18! Meanwhile, if you have any questions please contact us at

Best wishes,

Aline Deicke and Ingeborg van Vugt

Published by Ingeborg van Vugt
October 19, 2021

Stay tuned

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