Dynamic Fair Node Representation Learning

Many real-world networks such as social networks, traffic networks vary over time, which can be modeled as dynamic graphs. Despite the significant number of systems that can facilitate from the algorithmic tools over dynamic graphs, dynamic graph representation learning is an under-explored research area. Furthermore, while the fairness of algorithms is essential for their deployment in real-world systems, this issue has never been considered in the context of dynamic graphs to the best of our knowledge. Motivated by this, the present study proposes an efficient online node representation learning framework over dynamic graphs that can also mitigate bias. Specifically, the proposed technique combines different observations (graph structure and nodal attributes) of the same source (attributed graph) in a complementary way while also reducing the intrinsic bias in the learned representations. Experimental results on dynamic graphs show that the proposed online strategy can improve the group fairness measures for node classification together with comparable/better utility to the baselines.