Fuzzy Representation Learning on Dynamic Graphs

Exploring dynamic patterns from complex and large-scale networks is a significant and challenging task in graph analysis. One of the most advanced solutions is dynamic graph representation learning, which embeds structural and temporal correlations into a representative vector for each node or subgraph. Existing models have made some successes, such as overcoming the problems of induction for unseen nodes and scalability for large-scale evolving networks. However, these models usually rely on crisp representation learning that is incapable of modeling feature fuzziness and capturing uncertainties in dynamic graphs. While real-world dynamic networks as complex systems always contain non-negligible but inestimable uncertainties in node/link attributes and network topology. These uncertainties may cause the learned representations from crisp models hard to precisely reflect network evolution. To address the issues, we propose a new dynamic graph representation learning model, called FuzzyDGL, which first incorporates fuzzy representation learning to handle the uncertainties in dynamic graphs. Through combining CDGRL with fuzzy logic, the FuzzyDGL digests both of their advantages. On the one hand, it has flexible model scalability and brilliant inductive capability. On the other hand, it can model feature fuzziness to reduce the impact of uncertainties in dynamic graphs, improving the quality of learned representations. To demonstrate its effectiveness, we conduct two important tasks of network analysis, including link prediction and node classification, over eight real-world datasets. The experimental results show the strong competitiveness and generalization of the FuzzyDGL against a number of baseline models.

Automated summary

This paper proposes a new dynamic graph representation learning model called FuzzyDGL, which incorporates fuzzy representation learning to handle uncertainties in dynamic graphs. Experimental results show that FuzzyDGL has strong competitiveness and generalization in tasks like link prediction and node classification.