A Unified Seeding Framework.

The central theme of this paper is to answer how gender differences and similarities can impact the information spreading process. The analysis is based on two datasets: a large-scale Instagram data and a small-scale Facebook data. We explore various centrality measures, focusing on single hop interactions, i.e., intensity, degree, to multi-hop interactions, i.e., Pagerank, HI-index, and embedding index based on graph neural networks. OAn extensive simulation comparison with target-agnostic algorithms shows that the proposed Disparity can disseminate information according to the disparity requirement while effectively maximizing the information spread.