  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.
