The efficient processing of large graphs is challenging. Given the current data availability, real network traces are growing in variety and volume turning imperative the design of solutions and systems based on parallel and distributed technologies. In this sense, high performance methodologies may potentially leverage graph processing community similarly to what they have accomplished in scientific computing. However, despite of the high performance computing success in computing-demanding scientific applications, graph analytics suffers from distinct and inherent difficulties given its data-driven nature, e.g, high data-access computing ratio and poor locality of memory access. Furthermore, as one's analysis requires a more complex infrastructure, the development effort for crafting new solutions from scratch turns imperative the adoption of software platforms --- DBMS, frameworks and libraries --- to alleviate the development burden. In this scenario, many platforms data oriented and graph specific have been proposed. We estimate that there are currently more than 180 platforms that can be employed in graph analytic tasks.