  Link prediction algorithms aim to infer the existence of connections (or
links) between nodes in network-structured data and are typically applied to
refine the connectivity among nodes. In this work, we focus on link prediction
for flow-driven spatial networks, which are embedded in a Euclidean space and
relate to physical exchange and transportation processes (e.g., blood flow in
vessels or traffic flow in road networks). To this end, we propose the Graph
Attentive Vectors (GAV) link prediction framework. GAV models simplified
dynamics of physical flow in spatial networks via an attentive,
neighborhood-aware message-passing paradigm, updating vector embeddings in a
constrained manner. We evaluate GAV on eight flow-driven spatial networks given
by whole-brain vessel graphs and road networks. GAV demonstrates superior
performances across all datasets and metrics and outperformed the
state-of-the-art on the ogbl-vessel benchmark at the time of submission by 12%
(98.38 vs. 87.98 AUC). All code is publicly available on GitHub.
