# Modeling Attention Flow on Graphs

arXiv: https://arxiv.org/abs/1811.00497 · HTML: https://mlsanity.com/paper/1811.00497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [Search to Pass Messages for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2210.16740/) (2022)
- [Attention-based graph neural networks: a survey](https://mlsanity.com/paper/2605.08679/) (2026)

## Abstract (arXiv)

Real-world scenarios demand reasoning about process, more than final outcome prediction, to discover latent causal chains and better understand complex systems. It requires the learning algorithms to offer both accurate predictions and clear interpretations. We design a set of trajectory reasoning tasks on graphs with only the source and the destination observed. We present the attention flow mechanism to explicitly model the reasoning process, leveraging the relational inductive biases by basing our models on graph networks. We study the way attention flow can effectively act on the underlying information flow implemented by message passing. Experiments demonstrate that the attention flow driven by and interacting with graph networks can provide higher accuracy in prediction and better interpretation for trajectory reasoning.