Ricerc@Sapienza

Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention. In this paper, we address the problem of identifying a densest subgraph, while ensuring that none of one binary protected attribute is disparately impacted. Unfortunately, the underlying algorithmic problem is NP-hard, even in its approximation version: approximating the densest fair subgraph with a polynomial-time algorithm is at least as hard as the densest subgraph problem of at most k vertices, for which no constant approximation algorithms are known.

In this paper, we design and test a primary–secondary user resource-management controller in cognitive radio vehicular networks, under hard and soft collision constraints. We cast the resource-management problem into a stochastic network utility maximization problem and derive the optimal steady-state controllers, which adaptively allocate the access time-windows to the secondary-users. We derive closed form expressions for the throughput-gain of the general controller with respect to the memoryless one, discussing conditions of applicability and advantages of each subclass.