TE-MDG: Transfer Entropy for Mutation-aware Dynamic Graph Representation Learning

Dynamic graph representation learning aims to capture the temporal patterns of evolving graphs, which is critical for time-aware applications like social networks and recommendation systems. Most of existing methods typically model long-term dynamics by aggregating the nodes' historical behaviors. However, in real-world dynamic graphs, node features and their interactions often evolve with two types of mutations: Rational Mutations that are consistent with stable evolution patterns and Occasional Mutations that are incidental and irrelevant. Recent advances fail to distinguish different types of mutations due to the absence of a clear standard, causing the meaningful information in mutations to be weakened and hard to extract. To this end, we propose a **T**ransfer **E**ntropy for **M**utation-aware **D**ynamic **G**raph Representation Learning framework (**TE-MDG**), which selectively incorporates different types of mutations into node representations. Specifically, TE-MDG consists of two key components: (i) Evolution Pattern Decoupling, which learns a set of temporal prototypes that capture the stable evolution patterns and serve as a guiding standard for distinguishing rational mutations from occasional ones; and (ii) Mutation Filtering, which uses transfer entropy to measure and strengthen the information gain from temporal prototypes to mutations, thereby encouraging the model to retain rational mutations while filtering out occasional ones. Extensive experiments on multiple real-world benchmark datasets demonstrate that TE-MDG is more effective than state-of-the-art baselines. The code and datasets are anonymously available at https://anonymous.4open.science/r/TE_MDG.