DynGEM: Deep Embedding Method for Dynamic Graphs

Understand

Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node classification.

• Existing methods focus on computing the embedding for static graphs.
• However, many graphs in practical applications are dynamic and evolve constantly over time.
• Naively applying existing embedding algorithms to each snapshot of dynamic graphs independently usually leads to unsatisfactory performance in terms of stability, flexibility and efficiency.