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In social networks, a node's position is a form of \it{social capital}.Better-positioned members not only benefit from (faster) access to diverseinformation, but innately have more potential influence on information spread.Structural biases often arise from network formation, and can lead tosignificant disparities in information access based on position. Further,processes such as link recommendation can exacerbate this inequality by relyingon network structure to augment connectivity.  We argue that one can understand and quantify this social capital through thelens of information flow in the network. We consider the setting where allnodes may be sources of distinct information, and a node's (dis)advantage deemsits ability to access all information available on the network. We introducethree new measures of advantage (broadcast, influence, and control), which arequantified in terms of position in the network using \it{access signatures} --vectors that represent a node's ability to share information. We then considerthe problem of improving equity by making interventions to increase the accessof the least-advantaged nodes. We argue that edge augmentation is mostappropriate for mitigating bias in the network structure, and frame a budgetedintervention problem for maximizing minimum pairwise access.  Finally, we propose heuristic strategies for selecting edge augmentations andempirically evaluate their performance on a corpus of real-world socialnetworks. We demonstrate that a small number of interventions significantlyincrease the broadcast measure of access for the least-advantaged nodes (over 5times more than random), and also improve the minimum influence. Additionalanalysis shows that these interventions can also dramatically shrink the gap inadvantage between nodes (over \%82) and reduce disparities between their accesssignatures.

Reducing Access Disparities in Networks using Edge Augmentation