tBDFS: Temporal Graph Neural Network Leveraging DFS
This work addresses temporal graph prediction tasks, offering a new perspective for researchers in graph neural networks, though it appears incremental as it builds on existing temporal GNN methods.
The paper tackles the problem of temporal graph neural networks by proposing tBDFS, a novel architecture that aggregates information from temporal paths using a depth-first search approach, achieving favorable performance on multiple link prediction tasks compared to state-of-the-art baselines.
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.