arXiv subjects

Annabell Berger

Publications and source records attributed to Annabell Berger.

10 recordsLinked to original sources

arXiv2018

Smaller Universes for Uniform Sampling of 0,1-matrices with fixed row and column sums

An important problem arising in the study of complex networks, for instance in community detection and motif finding, is the sampling of graphs with fixed degree sequence. The equivalent problem of generating random 0,1 matrices with fixed row and column sums is frequently used as a quantitative tool in ecology. It has however proven very challenging to design sampling algorithms that are both fast and unbiased. This article focusses on Markov chain approaches for sampling, where a close-to-random graph is produced by applying a large number N of small changes to a given graph. Examples are the switch chain and Curveball chain, which are both commonly used by practitioners as they are easy to implement and known to sample unbiased when N is large enough. Within theoretical research, much effort has gone into proving bounds on N. However, existing theoretical bounds are impractically large for most applications while experiments suggest that much fewer steps are needed to obtain a good sample. The contribution of this article is twofold. Firstly it is a step towards better understanding of the discrepancy between experimental observations and theoretically proven bounds. In particular, we argue that while existing Markov chain algorithms run on the set of all labelled graphs with a given degree sequence, node labels are unimportant in practice and are usually ignored in determining experimental bounds. We prove that ignoring node labels corresponds to projecting a Markov chain onto equivalence classes of isomorphic graphs and that the resulting projected Markov chain converges to its stationary distribution at least as fast as the original Markov chain. Often convergence is much faster, as we show in examples, explaining part of the difference between theory and experiments...

math.CO↗

arXiv2018

A unifying framework for fast randomization of ecological networks with fixed (node) degrees

The switching model is a Markov chain approach to sample graphs with fixed degree sequence uniformly at random. The recently invented Curveball algorithm for bipartite graphs applies several switches simultaneously (`trades'). Here, we introduce Curveball algorithms for simple (un)directed graphs which use single or simultaneous trades. We show experimentally that these algorithms converge magnitudes faster than the corresponding switching models.

math.CO↗

arXiv2017

Sampling bipartite graphs with given vertex degrees and fixed edges and non-edges

We consider the problem of sampling a bipartite graph with given vertex degrees where a set $F$ of edges and non-edges which need to be contained is predefined. Our general result shows that the repeated swap of edges and non-edges in alternating cycles of at most size $2\ell-2$ ('$j$-swaps' with $j \leq 2 \ell-2$) in a current graph lead to an ergodic Metropolis Markov chain whenever $F$ does not contain a cycle of length $2 \ell$ with $\ell \geq 4.$ This leads to useful Markov chains whenever $\ell$ is not too large. If $F$ is a forest, $4$- and $6$-swaps are sufficient. Furthermore, we prove that $4$-swaps are sufficient when $F$ does not contain a matching of size $3.$ We extend the Curveball algorithm of Strona et al. \cite{Strona2014b} to our cases.

math.CO↗

arXiv2017

The isomorphic version of Brualdies nestedness is in P

The discrepancy BR for an $m \times n$ $0,1$-matrix from Brualdi and Sanderson \cite{Brualdi1998} counts the minimum number of $1$'s which need to be shifted in each row to the left to achieve its Ferrers matrix, i.e. each row consists of consecutive $1$'s followed by consecutive $0$'s. For ecological bipartite networks BR describes how nested a set of relationships is. Since different labeled matrices can be isomorphic but possess different discrepancies, we define a metric determining the minimum discrepancy in an isomorphic class. We give a reduction to $k\leq n$ minimum weighted perfect matching problems.

math.CO↗

arXiv2016

Marathon: An open source software library for the analysis of Markov-Chain Monte Carlo algorithms

In this paper, we consider the Markov-Chain Monte Carlo (MCMC) approach for random sampling of combinatorial objects. The running time of such an algorithm depends on the total mixing time of the underlying Markov chain and is unknown in general. For some Markov chains, upper bounds on this total mixing time exist but are too large to be applicable in practice. We try to answer the question, whether the total mixing time is close to its upper bounds, or if there is a significant gap between them. In doing so, we present the software library marathon which is designed to support the analysis of MCMC based sampling algorithms. The main application of this library is to compute properties of so-called state graphs which represent the structure of Markov chains. We use marathon to investigate the quality of several bounding methods on four well-known Markov chains for sampling perfect matchings and bipartite graph realizations. In a set of experiments, we compute the total mixing time and several of its bounds for a large number of input instances. We find that the upper bound gained by the famous canonical path method is several magnitudes larger than the total mixing time and deteriorates with growing input size. In contrast, the spectral bound is found to be a precise approximation of the total mixing time.

cs.DM↗

arXiv2014

The Connection between the Number of Realizations for Degree Sequences and Majorization

The \emph{graph realization problem} is to find for given nonnegative integers $a_1,\dots,a_n$ a simple graph (no loops or multiple edges) such that each vertex $v_i$ has degree $a_i.$ Given pairs of nonnegative integers $(a_1,b_1),\dots,(a_n,b_n),$ (i) the \emph{bipartite realization problem} ask whether there is a bipartite graph (no loops or multiple edges) such that vectors $(a_1,...,a_n)$ and $(b_1,...,b_n)$ correspond to the lists of degrees in the two partite sets, (ii) the \emph{digraph realization problem} is to find a digraph (no loops or multiple arcs) such that each vertex $v_i$ has indegree $a_i$ and outdegree $b_i.$\\ The classic literature provides characterizations for the existence of such realizations that are strongly related to the concept of majorization. Aigner and Triesch (1994) extended this approach to a more general result for graphs, leading to an efficient realization algorithm and a short and simple proof for the Erdős-Gallai Theorem. We extend this approach to the bipartite realization problem and the digraph realization problem.\\ Our main result is the connection between majorization and the number of realizations for a degree list in all three problems. We show: if degree list $S'$ majorizes $S$ in a certain sense, then $S$ possesses more realizations than $S'.$ We prove that constant lists possess the largest number of realizations for fixed $n$ and a fixed number of arcs $m$ when $n$ divides $m.$ So-called \emph{minconvex lists} for graphs and bipartite graphs or \emph{opposed minconvex lists} for digraphs maximize the number of realizations when $n$ does not divide $m$.

math.CO↗

arXiv2014

Broder's Chain Is Not Rapidly Mixing

We prove that Broder's Markov chain for approximate sampling near-perfect and perfect matchings is not rapidly mixing for Hamiltonian, regular, threshold and planar bipartite graphs, filling a gap in the literature. In the second part we experimentally compare Broder's chain with the Markov chain by Jerrum, Sinclair and Vigoda from 2004. For the first time, we provide a systematic experimental investigation of mixing time bounds for these Markov chains. We observe that the exact total mixing time is in many cases significantly lower than known upper bounds using canonical path or multicommodity flow methods, even if the structure of an underlying state graph is known. In contrast we observe comparatively tighter upper bounds using spectral gaps.

cs.DM↗

arXiv2013

A Note on the Characterization of Digraph Sequences

We consider the following fundamental realization problem of directed graphs. Given a sequence $S:={a_1 \choose b_1},\dots,{a_n \choose b_n}$ with $a_i,b_i\in \mathbb{Z}_0^+.$ Does there exist a digraph (no loops and no parallel arcs are allowed)$G=(V,A)$ with a labeled vertex set $V:=\{v_1,\dots,v_n\}$ such that for all $v_i \in V$ indegree and outdegree of $v_i$ match exactly the given numbers $a_i$ and $b_i$, respectively? There exist two known approaches solving this problem in polynomial running time. One first approach of Kleitman and Wang (1973) uses recursive algorithms to construct digraph realizations \cite{KleitWang:73}. The second one draws back into the Fifties and Sixties of the last century and gives a complete characterization of digraph sequences (Gale 1957, Fulkerson 1960, Ryser 1957, Chen 1966). That is, one has only to validate a certain number of inequalities. Chen bounded this number by $n$. His characterization demands the property that $S$ has to be in lexicographical order. We show that this condition is stronger than necessary. We provide a new characterization which is formally analogous to the classical one by Erd{\H o}s and Gallai (1960) for graphs. Hence, we can give several, different sets of $n$ inequalities. We think that this stronger result can be very important with respect to structural insights about the sets of digraph sequences, for example in the context of threshold sequences. Furthermore, the number of inequalities can be restricted to all $k \in \{1,\dots,n-1\}$ with $a_{k+1}>a_{k}$ and to $k=n.$ An analogous result for graphs was given by Tripathi and Vijay \cite{TripathiVijay03}. We prove this property also for the case of digraphs (no parallel arcs) with at most one loop per vertex.

math.CO↗

arXiv2012

How to Attack the NP-complete Dag Realization Problem in Practice

We study the following fundamental realization problem of directed acyclic graphs (dags). Given a sequence S:=(a_1,b_1),...,(a_n, b_n) with a_i, b_i in Z_0^+, does there exist a dag (no parallel arcs allowed) with labeled vertex set V:= {v_1,...,v_n} such that for all v_i in V indegree and outdegree of v_i match exactly the given numbers a_i and b_i, respectively? Recently this decision problem has been shown to be NP-complete by Nichterlein (2011). However, we can show that several important classes of sequences are efficiently solvable. In previous work (Berger and Mueller-Hannemann, FCT2011), we have proved that yes-instances always have a special kind of topological order which allows us to reduce the number of possible topological orderings in most cases drastically. This leads to an exact exponential-time algorithm which significantly improves upon a straightforward approach. Moreover, a combination of this exponential-time algorithm with a special strategy gives a linear-time algorithm. Interestingly, in systematic experiments we observed that we could solve a huge majority of all instances by the linear-time heuristic. This motivates us to develop characteristics like dag density and "distance to provably easy sequences" which can give us an indicator how easy or difficult a given sequence can be realized. Furthermore, we propose a randomized algorithm which exploits our structural insight on topological sortings and uses a number of reduction rules. We observe that it clearly outperforms all other variants and behaves surprisingly well for almost all instances. Another striking observation is that our simple linear-time algorithm solves a set of real-world instances from different domains, namely ordered binary decision diagrams (OBDDs), train and flight schedules, as well as instances derived from food-web networks without any exception.

cs.DS↗

arXiv2010

Uniform sampling of undirected and directed graphs with a fixed degree sequence

Many applications in network analysis require algorithms to sample uniformly at random from the set of all graphs with a prescribed degree sequence. We present a Markov chain based approach which converges to the uniform distribution of all realizations for both the directed and undirected case. It remains an open challenge whether these Markov chains are rapidly mixing. For the case of directed graphs, we also explain in this paper that a popular switching algorithm fails in general to sample uniformly at random because the state graph of the Markov chain decomposes into different isomorphic components. We call degree sequences for which the state graph is strongly connected arc swap sequences. To handle arbitrary degree sequences, we develop two different solutions. The first uses an additional operation (a reorientation of induced directed 3-cycles) which makes the state graph strongly connected, the second selects randomly one of the isomorphic components and samples inside it. Our main contribution is a precise characterization of arc swap sequences, leading to an efficient recognition algorithm. Finally, we point out some interesting consequences for network analysis.

cs.DM↗