  Graph is a useful data structure to model various real life aspects like
email communications, co-authorship among researchers, interactions among
chemical compounds, and so on. Supporting such real life interactions produce a
knowledge rich massive repository of data. However, efficiently understanding
underlying trends and patterns is hard due to large size of the graph.
Therefore, this paper presents a scalable compression solution to compute
summary of a weighted graph. All the aforementioned interactions from various
domains are represented as edge weights in a graph. Therefore, creating a
summary graph while considering this vital aspect is necessary to learn
insights of different communication patterns. By experimenting the proposed
method on two real world and publically available datasets against a state of
the art technique, we obtain order of magnitude performance gain and better
summarization accuracy.

Data Structures and Algorithms