Affiliation:
1. IBM Almaden Research Center, USA
2. IDSIA, USI-SUPSI, Switzerland
3. MIT, USA
Abstract
Measuring the importance of a node in a network is a major goal in the analysis of social networks, biological systems, transportation networks, and so forth. Different
centrality
measures have been proposed to capture the notion of node importance. For example, the
center
of a graph is a node that minimizes the maximum distance to any other node (the latter distance is the
radius
of the graph). The
median
of a graph is a node that minimizes the sum of the distances to all other nodes. Informally, the
betweenness centrality
of a node
w
measures the fraction of shortest paths that have
w
as an intermediate node. Finally, the
reach centrality
of a node
w
is the smallest distance
r
such that any
s
-
t
shortest path passing through
w
has either
s
or
t
in the ball of radius
r
around
w
.
The fastest known algorithms to compute the center and the median of a graph and to compute the betweenness or reach centrality even of a single node take roughly cubic time in the number
n
of nodes in the input graph. It is open whether these problems admit truly subcubic algorithms, i.e., algorithms with running time Õ(n
3-δ
) for some constant δ > 0.
1
We relate the complexity of the mentioned centrality problems to two classical problems for which no truly subcubic algorithm is known, namely All Pairs Shortest Paths (APSP) and Diameter. We show that Radius, Median, and Betweenness Centrality are
equivalent under subcubic reductions
to APSP, i.e., that a truly subcubic algorithm for any of these problems implies a truly subcubic algorithm for all of them. We then show that Reach Centrality is equivalent to Diameter under subcubic reductions. The same holds for the problem of approximating Betweenness Centrality within any finite factor. Thus, the latter two centrality problems could potentially be solved in truly subcubic time, even if APSP required essentially cubic time.
On the positive side, our reductions for Reach Centrality imply an improved Õ(Mn
ω
)-time algorithm for this problem in case of non-negative integer weights upper bounded by
M
, where ω is a fast matrix multiplication exponent.
Funder
SNSF Excellence
NSF CAREER
BSF
Sloan Research Fellowship and a Google
Publisher
Association for Computing Machinery (ACM)
Subject
Mathematics (miscellaneous)
Cited by
2 articles.
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1. Investment network and stock’s systemic risk contribution: Evidence from China;The Quarterly Review of Economics and Finance;2024-04
2. Super-Logarithmic Lower Bounds for Dynamic Graph Problems;2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS);2023-11-06