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

翻译：信息通过社交网络的传播无处不在。影响力最大化算法旨在识别那些在获取信息后能在社交网络中产生最广泛传播的个体，此类算法主要针对营销场景开发。在极为常见的具有社区结构的社交网络中，仅专注于最大化传播范围的影响力算法可能导致社区间信息覆盖率的显著差异，这在公共卫生信息传播等场景中会产生问题。虽然部分影响力算法试图利用节点属性来弥补信息覆盖率差异，但尚未有算法利用网络自身的内在社区结构——而利用这一结构可能是有益的，因为社区直接影响信息的传播路径。此外，运用经验网络结构使我们能够借助社区检测技术，从而在缺乏相关节点属性或节点属性无法准确反映网络社区结构的情况下，仍能运行公平感知算法。与其它公平影响力算法不同，本研究通过为社交网络拟合模型，进而确定实现最优公平信息传播的种子分配策略。我们开发了一种算法，用于确定基于最大熵定义的预期公平覆盖率的最优种子分配方案，在适当条件下提供了理论保证，并在模拟网络和真实网络上验证了其实证准确性。由于该算法依赖拟合的网络模型而非网络本身，因此特别适用于部分观测和含噪的社交网络。