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Information spread through social networks is ubiquitous. Influence maximiza-tion (IM) algorithms aim to identify individuals who will generate the greatestspread through the social network if provided with information, and have beenlargely devel- oped with marketing in mind. In social networks with communitystructure, which are very common, IM algorithms focused solely on maximizingspread may yield signifi- cant disparities in information coverage betweencommunities, which is problematic in settings such as public health messaging.While some IM algorithms aim to remedy disparity in information coverage usingnode attributes, none use the empirical com- munity structure within thenetwork itself, which may be beneficial since communities directly affect thespread of information. Further, the use of empirical network struc- ture allowsus to leverage community detection techniques, making it possible to runfair-aware algorithms when there are no relevant node attributes available, orwhen node attributes do not accurately capture network community structure. Incontrast to other fair IM algorithms, this work relies on fitting a model tothe social network which is then used to determine a seed allocation strategyfor optimal fair information spread. We develop an algorithm to determineoptimal seed allocations for expected fair coverage, defined through maximumentropy, provide some theoretical guarantees under appropriate conditions, anddemonstrate its empirical accuracy on both simu- lated and real networks.Because this algorithm relies on a fitted network model and not on the networkdirectly, it is well-suited for partially observed and noisy social networks.

Fair Information Spread on Social Networks with Community Structure