  Temporal graph neural networks (temporal GNNs) have been widely researched,
reaching state-of-the-art results on multiple prediction tasks. A common
approach employed by most previous works is to apply a layer that aggregates
information from the historical neighbors of a node. Taking a different
research direction, in this work, we propose tBDFS -- a novel temporal GNN
architecture. tBDFS applies a layer that efficiently aggregates information
from temporal paths to a given (target) node in the graph. For each given node,
the aggregation is applied in two stages: (1) A single representation is
learned for each temporal path ending in that node, and (2) all path
representations are aggregated into a final node representation. Overall, our
goal is not to add new information to a node, but rather observe the same exact
information in a new perspective. This allows our model to directly observe
patterns that are path-oriented rather than neighborhood-oriented. This can be
thought as a Depth-First Search (DFS) traversal over the temporal graph,
compared to the popular Breath-First Search (BFS) traversal that is applied in
previous works. We evaluate tBDFS over multiple link prediction tasks and show
its favorable performance compared to state-of-the-art baselines. To the best
of our knowledge, we are the first to apply a temporal-DFS neural network.
