Topological Fairness in Graph Representation Learning Based on Edge Differentiation

The fairness is a fundamental problem in graph representation learning (GRL) because it can lead to the failure of downstream tasks for the significant deviations between the results of GRL and the true semantics. However, current research on fairness mainly focuses on the sensitive attributes of node, ignoring the bias caused by the unfairness of the topological structure of the graph. Although some studies usually attribute topological unfairness to homogeneity effects, that is, nodes with the same sensitive attributes are more likely to form connections, the role of topological fairness in graph representation learning still needs to be explored. To address this issue, we first define Topological Fairness Graph (TFG) inspired by the traditional concepts of Statistical Parity and Equal Opportunity. Then, based on TFG, Edge Differential Operator for Topological Fairness (EDO-TF) in Graph representation learning is introduced to measure the variation of topological structure of graph with Probabilistic Distribution Disparit. Finally, we combine the EDO-TF into the loss function in GRL to eliminate the topological unfairness of graph. Our empirical results are tested on eight datasets, indicating that the introduction of TFG can correct many erroneous information orientations, which is a great extension of research on graph fairness solely based on feature information.