Category: Tutorial
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Temporal Graph Neural Networks
Introduction Due to the increasing connectivity in the world, graph data has manifested itself in many contexts, particularly in recent years. Analyzing interactions within graphs makes for an interesting exploration across many applications. While many studies concentrate on static graphs, the changing nature of data requires examining how interactions within a graph change over time.
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Materials as Graphs
Introduction Graph Neural Networks (GNNs) have emerged as a critical tool for modeling complex relationships and interactions in data, particularly in fields such as social and physical sciences. In this blog, we delve into the applications of GNNs in materials science (MS), an interdisciplinary field that combines elements of physics, chemistry, and engineering to understand
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De Bruijn Graphs
Introduction De Bruijn graphs, named after Dutch mathematician Nicolaas Govert de Bruijn, are invaluable in both computer science and bioinformatics. These graphs efficiently represent overlaps between sequences of symbols, making them essential for sequence analysis applications. While their utility spans various fields, they are predominantly applied in bioinformatics today. We have even recently demonstrated that
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