ﻻ يوجد ملخص باللغة العربية
We consider the problem of sampling and approximately counting an arbitrary given motif $H$ in a graph $G$, where access to $G$ is given via queries: degree, neighbor, and pair, as well as uniform edge sample queries. Previous algorithms for these tasks were based on a decomposition of $H$ into a collection of odd cycles and stars, denoted $mathcal{D}^*(H)={O_{k_1}, ldots, O_{k_q}, S_{p_1}, ldots, S_{p_ell}}$. These algorithms were shown to be optimal for the case where $H$ is a clique or an odd-length cycle, but no other lower bounds were known. We present a new algorithm for sampling and approximately counting arbitrary motifs which, up to $textrm{poly}(log n)$ factors, is always at least as good as previous results, and for most graphs $G$ is strictly better. The main ingredient leading to this improvement is an improved uniform algorithm for sampling stars, which might be of independent interest, as it allows to sample vertices according to the $p$-th moment of the degree distribution. Finally, we prove that this algorithm is emph{decomposition-optimal} for decompositions that contain at least one odd cycle. These are the first lower bounds for motifs $H$ with a nontrivial decomposition, i.e., motifs that have more than a single component in their decomposition.
In the subgraph counting problem, we are given a input graph $G(V, E)$ and a target graph $H$; the goal is to estimate the number of occurrences of $H$ in $G$. Our focus here is on designing sublinear-time algorithms for approximately counting occurr
In this work, we consider the problem of sampling a $k$-clique in a graph from an almost uniform distribution in sublinear time in the general graph query model. Specifically the algorithm should output each $k$-clique with probability $(1pm epsilon)
We present a near-tight analysis of the average query complexity -- `a la Nguyen and Onak [FOCS08] -- of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito [STOC09]. For any $n$-vertex graph of ave
We study the problem of approximating the eigenspectrum of a symmetric matrix $A in mathbb{R}^{n times n}$ with bounded entries (i.e., $|A|_{infty} leq 1$). We present a simple sublinear time algorithm that approximates all eigenvalues of $A$ up to a
The problem of sparsifying a graph or a hypergraph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Benczur and Karger (1996) showed that given any $n$-vertex undirected weigh