  In recent work, [1] introduced the concept of using a Block Adjacency Matrix
(BA) for the representation of spatio-temporal data. While their method
successfully concatenated adjacency matrices to encapsulate spatio-temporal
relationships in a single graph, it formed a disconnected graph. This
limitation hampered the ability of Graph Convolutional Networks (GCNs) to
perform message passing across nodes belonging to different time steps, as no
temporal links were present. To overcome this challenge, we introduce an
encoder block specifically designed to learn these missing temporal links. The
encoder block processes the BA and predicts connections between previously
unconnected subgraphs, resulting in a Spatio-Temporal Block Adjacency Matrix
(STBAM). This enriched matrix is then fed into a Graph Neural Network (GNN) to
capture the complex spatio-temporal topology of the network. Our evaluations on
benchmark datasets, surgVisDom and C2D2, demonstrate that our method, with
slightly higher complexity, achieves superior results compared to
state-of-the-art results. Our approach's computational overhead remains
significantly lower than conventional non-graph-based methodologies for
spatio-temporal data.
