  Graph representation learning (also known as network embedding) has been
extensively researched with varying levels of granularity, ranging from nodes
to graphs. While most prior work in this area focuses on node-level
representation, limited research has been conducted on graph-level embedding,
particularly for dynamic or temporal networks. However, learning
low-dimensional graph-level representations for dynamic networks is critical
for various downstream graph retrieval tasks such as temporal graph similarity
ranking, temporal graph isomorphism, and anomaly detection. In this paper, we
present a novel method for temporal graph-level embedding that addresses this
gap. Our approach involves constructing a multilayer graph and using a modified
random walk with temporal backtracking to generate temporal contexts for the
graph's nodes. We then train a "document-level" language model on these
contexts to generate graph-level embeddings. We evaluate our proposed model on
five publicly available datasets for the task of temporal graph similarity
ranking, and our model outperforms baseline methods. Our experimental results
demonstrate the effectiveness of our method in generating graph-level
embeddings for dynamic networks.
