tBDFS: Temporal Graph Neural Network Leveraging DFS

• URL: http://arxiv.org/abs/2206.05692v1
• Date: Sun, 12 Jun 2022 08:38:06 GMT
• Title: tBDFS: Temporal Graph Neural Network Leveraging DFS
• Abstract summary: 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. Overall, our goal is not to add new information to a node, but rather observe the same exact information in a new perspective.
• Score: 33.762062743040495
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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