Graph analytics is a vital field of research for representing relations between different entities and understanding patterns of interactions for large groups. The scope of large-scale graph analytics is complex enough that there exist numerous challenges and a plethora of frameworks, with each addressing a subset of challenges. In this paper, we explore the area of graph analytics and its available frameworks. In specific, we discuss the topology of graph algorithms, their applications, and their drawbacks. We then explore the existing analytics frameworks for solving these algorithms in two broad classes. We first classify them based on the target architecture, and then we classify them based on the type of workload they address. Since the performance of analytics is highly sensitive to the nature of the problem, there is not a single framework that addresses all classes of graph problems. This paper can help readers gain a better understanding of graph analytics and provides general guidance for choosing the modeling methods and the right frameworks that are suitable to particular graph problems.