From the abstract: "In this work, we present GraphChi, a disk-based system
for computing efficiently on graphs with billions of edges.
By using a well-known method to break large graphs into
small parts, and a novel parallel sliding windows method,
GraphChi is able to execute several advanced data mining,
graph mining, and machine learning algorithms on very
large graphs, using just a single consumer-level computer. ... By repeating experiments reported for existing distributed
systems, we show that, with only fraction of the
resources, GraphChi can solve the same problems in very
reasonable time."
Section 7.2 compares themselves to distributed graph frameworks.
From the abstract: "In this work, we present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. By using a well-known method to break large graphs into small parts, and a novel parallel sliding windows method, GraphChi is able to execute several advanced data mining, graph mining, and machine learning algorithms on very large graphs, using just a single consumer-level computer. ... By repeating experiments reported for existing distributed systems, we show that, with only fraction of the resources, GraphChi can solve the same problems in very reasonable time."
Section 7.2 compares themselves to distributed graph frameworks.