Compressed Representation of Web and Social Networks via Dense Subgraphs
Cecilia Hernández and Gonzalo Navarro
Mining and analyzing large web and social networks are challenging tasks in
storage and information access.
In order to address this problem, several works have proposed compressing
allowing neighbor access over their compressed representations.
In this paper, we propose a novel compressed structure aiming to reduce
and support efficient navigation over web and social graph compressed
Our approach uses clustering and mining for finding dense subgraphs and
represents them using compact data structures.
We perform experiments using a wide range of web and
social networks and compare our results with the best known techniques. Our
results show that
we improve the state of the art space/time tradeoffs for supporting neighbor
Our compressed structure also
enables mining queries based on dense subgraphs, such as cliques and