The densest subgraph problem in a graph (\dsg), in the simplest
form, is the following. Given an undirected graph $G=(V,E)$ find a
subset $S \subseteq V$ of vertices that maximizes the ratio
$|E(S)|/|S|$ where $E(S)$ is the set of edges with both endpoints
in $S$. \dsg and several of its variants are well-studied in theory
and practice and have many applications in data mining and network
analysis. In this paper we study fast algorithms and structural
aspects of \dsg via the lens of \emph{supermodularity}.
For this we consider the densest supermodular subset problem (\dssp):
given a non-negative supermodular function $f: 2^V
\rightarrow \mathbb{R}_+$, maximize $f(S)/|S|$.
For \dsg we describe a simple flow-based algorithm that outputs a
$(1-\eps)$-approximation in deterministic $\tilde{O}(m/\eps)$ time
where $m$ is the number of edges. Our algorithm is the first to have
a near-linear dependence on $m$ and $1/\eps$ and improves previous
methods based on an LP relaxation. It generalizes to hypergraphs, and
also yields a faster algorithm for directed \dsg.
Greedy peeling algorithms have been very popular for \dsg and
several variants due to their efficiency, empirical performance, and
worst-case approximation guarantees. We describe a simple peeling
algorithm for \dssp and analyze its approximation guarantee in a
fashion that unifies several existing results. Boob et
al.\ \cite{bgpstww-20} developed an \emph{iterative} peeling
algorithm for \dsg which appears to work very well in practice, and
made a conjecture about its convergence to optimality. We
affirmatively answer their conjecture, and in fact prove that a
natural generalization of their algorithm converges to a
$(1-\eps)$-approximation for \emph{any} supermodular function $f$;
the key to our proof is to consider an LP formulation that is
derived via the \Lovasz extension of a supermodular function. For
\dsg the bound on the number of iterations we prove is $O(\frac{\Delta
\ln |V|}{\lambda^*}\cdot \frac{1}{\eps^2})$ where $\Delta$ is
the maximum degree and $\lambda^*$ is the optimum value. Our work suggests
that iterative peeling can be an effective heuristic for
several objectives considered in the literature.
Finally, we show that the $2$-approximation for densest-at-least-$k$
subgraph \cite{ks-09} extends to the supermodular setting. We also
give a unified analysis of the peeling algorithm for this problem, and
via this analysis derive an approximation guarantee for a
generalization of \dssp to maximize $f(S)/g(|S|)$ for a concave
function $g$.
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A Graph Theoretic Additive Approximation of Optimal Transport
Transportation cost is an attractive similarity measure between probability distributions due to its many useful theoretical properties. However, solving optimal transport exactly can be prohibitively expensive. Therefore, there has been significant effort towards the design of scalable approximation algorithms. Previous combinatorial results [Sharathkumar, Agarwal STOC '12, Agarwal, Sharathkumar STOC '14] have focused primarily on the design of near-linear time multiplicative approximation algorithms. There has also been an effort to design approximate solutions with additive errors [Cuturi NIPS '13, Altschuler \etal\ NIPS '17, Dvurechensky \etal\, ICML '18, Quanrud, SOSA '19] within a time bound that is linear in the size of the cost matrix and polynomial in C/\delta; here C is the largest value in the cost matrix and \delta is the additive error. We present an adaptation of the classical graph algorithm of Gabow and Tarjan and provide a novel analysis of this algorithm that bounds its execution time by \BigO(\frac{n^2 C}{\delta}+ \frac{nC^2}{\delta^2}). Our algorithm is extremely simple and executes, for an arbitrarily small constant \eps, only \lfloor \frac{2C}{(1-\eps)\delta}\rfloor + 1 iterations, where each iteration consists only of a Dijkstra-type search followed by a depth-first search. We also provide empirical results that suggest our algorithm is competitive with respect to a sequential implementation of the Sinkhorn algorithm in execution time. Moreover, our algorithm quickly computes a solution for very small values of \delta whereas Sinkhorn algorithm slows down due to numerical instability.
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- Award ID(s):
- 1909171
- NSF-PAR ID:
- 10175764
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Page Range / eLocation ID:
- 13813--13823
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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