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Temporal Graphs: Embedding with Fairness
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Uriel Singer, Ph.D. Thesis Seminar
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Sunday, 3.4.2022, 11:45
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Zoom Lecture: 96690022933
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Advisor:  Dr. Kira Radinsky
In this thesis, we study temporal graphs and how to best represent their nodes and edges for multiple classification tasks. We first study the basics of how to represent nodes and edges in (un)weighted and (un)directed temporal graphs. We then present methods to leverage different aspects of temporal graphs, such as a temporal message passing and multiple attributes over edges. Finally, we study how bias manifests itself in temporal graphs and propose methods to balance accuracy and fairness in such graphs. We evaluate the effectiveness of our methods over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and node classification, comparing to competitive baselines and algorithmic alternatives.
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