Graphs are found in a plethora of domains, including online social networks, the World Wide Web and the study of epidemics, to name a few. With the advent of greater volumes of information and the need for continuously updated results under temporal constraints, it is necessary to explore novel approaches that further enable performance improvements. In the scope of stream processing over graphs, we research the trade-offs between result accuracy and the speedup of approximate computation techniques. We see this as a natural path towards these performance improvements. Herein we present \name, through which we conducted our research. We showcase an innovative model for approximate graph processing, implemented in \texttt{Apache Flink}. We analyze our model and evaluate it with the case study of the PageRank algorithm \cite{pageRank}, perhaps the most famous measure of vertex centrality used to rank websites in search engine results. %In light of our model, we discuss the challenges driven by relations between result accuracy and potential performance gains. Our experiments, even when set up for favoring \texttt{Flink} for comparability, show that \name can improve performance up to 3X speedups, while achieving result quality above 95\% when compared to results of the traditional version of PageRank without any summarization or approximation techniques.