Sublinear-Time Distributed Algorithms for Detecting Small Cliques and Even Cycles.
DISC(2019)
摘要
In this paper we give sublinear-time distributed algorithms in the
$$\mathsf {CONGEST}$$
model for finding or listing cliques and even-length cycles. We show for the first time that all copies of 4-cliques and 5-cliques in the network graph can be detected and listed in sublinear time,
$$O(n^{5/6+o(1)})$$
rounds and
$$O(n^{73/75+o(1)})$$
rounds, respectively. For even-length cycles,
$$C_{2k}$$
, we give an improved sublinear-time algorithm, which exploits a new connection to extremal combinatorics. For example, for 6-cycles we improve the running time from
$${\tilde{O}}(n^{5/6})$$
to
$${\tilde{O}}(n^{3/4})$$
rounds. We also show two obstacles on proving lower bounds for
$$C_{2k}$$
-freeness: first, we use the new connection to extremal combinatorics to show that the current lower bound of
$${\tilde{\varOmega }}(\sqrt{n})$$
rounds for 6-cycle freeness cannot be improved using partition-based reductions from 2-party communication complexity, the technique by which all known lower bounds on subgraph detection have been proven to date. Second, we show that there is some fixed constant
$$\delta \in (0,1/2)$$
such that for any k, a lower bound of
$$\varOmega (n^{1/2+\delta })$$
on
$$C_{2k}$$
-freeness would imply new lower bounds in circuit complexity. We use the same technique to show a barrier for proving any polynomial lower bound on triangle-freeness. For general subgraphs, it was shown by Fischer et al. that for any fixed k, there exists a subgraph H of size k such that H-freeness requires
$${\tilde{\varOmega }}(n^{2-\varTheta (1/k)})$$
rounds. It was left as an open problem whether this is tight, or whether some constant-sized subgraph requires truly quadratic time to detect. We show that in fact, for any subgraph H of constant size k, the H-freeness problem can be solved in
$$O(n^{2 - \varTheta (1/k)})$$
rounds, nearly matching the lower bound.
更多查看译文
关键词
Distributed computing,Subgraph freeness,Expander decomposition,CONGEST
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要