When considering relational problems, the temporal dimension is often crucial to understand whether the process behind the relational graph is evolving, and how; think how often people follow and unfollow each other on Instagram, how the type of content in one’s posts may change over time, and how all of these aspects are echoed throughout the network, interacting with one another in complex ways.
While most works that apply deep learning to temporal networks are focused on the evolution of the graph at the node or edge level, it is extremely interesting to study a graph-based process from a global perspective, at the graph level, to detect trends and changes in the process itself.
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