Abstract:
Graphs have always been an interesting structure to study in both mathematics and computer
science , and have become even more interesting in the context of online social
networks, recommendation networks whose underlying network structures are nicely
represented by graphs.The graphs are massive: Facebook social graph has billions of vertices and
web graphs are much larger.With “large” graphs comes the desire to extract meaningful information
from these graphs. In the age of multi-core CPUs and distributed computing, concurrent
processing of graphs proves to be an important topic.
Graph processing frameworks are being increasingly used to perform analysis on the enormous
graphs like follower graphs in online social networks,web graph,recommendation graphs
etc.Graphlab, FlashGraph, PowerGraph, X-stream are few frameworks are used to compute metrics
such as pageank,shortest path etc on graphs. The lack of access locality when traversing
edges makes it difficult to achieve good results in graph analysis.
To gain an understanding of how graph processing frameworks perform, we conduct a study to
experimentally compare Flash Graph and Graph lab Create using several metrics.The systems are
compared with three different algorithms (Page Rank,weakly connected components,and Triangle
counting) on single machine.Our evaluation shows that Graph lab create is performing better
than Flash Graph.
