ABSTRACT
In this paper, we leverage the concept of the metric backbone
to improve the efficiency of large-scale graph analytics. The
metric backbone is the minimum subgraph that preserves
the shortest paths of a weighted graph. We use the metric
backbone in place of the original graph to compute vari-
ous graph metrics exactly or with good approximation. By
computing on a smaller graph, we improve the performance
of graph analytics applications on two di erent systems, a
batch graph processing system and a graph database.
Further, we provide an algorithm for the computation of
the metric backbone on large graphs. While one can com-
pute the metric backbone by solving the all-pairs-shortest-
paths problem, this approach incurs prohibitive time and
space complexity for big graphs. Instead, we propose a
heuristic that makes computing the metric backbone prac-
tical even for large graphs. Additionally, we analyze several
real datasets of di erent sizes and domains and we show
that we can approximate the metric backbone by removing
only rst-order semi-metric edges; edges for which a shorter
two-hop path exists.
We provide a distributed implementation of our algorithm
and apply it in large scale scenarios. We evaluate our algo-
rithm using a variety of real graphs, including a Facebook
social network subgraph of 50 billion edges. We measure
the impact of using the metric backbone on runtime per-
formance in two graph management systems. We achieve
query speedups of up to 6.7x in the Neo4j commercial graph
database and job speedups of up to 6x in the Giraph graph
processing system.