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István Miklós

Publications and source records attributed to István Miklós.

27 records · Page 2Linked to original sources

Constructing, sampling and counting graphical realizations of restricted degree sequences

With the current burst of network theory (especially in connection with social and biological networks) there is a renewed interest on realizations of given degree sequences. In this paper we propose an essentially new degree sequence problem: we want to find graphical realizations of a given degree sequence on labeled vertices, where certain would-be edges are {\em forbidden}. Then we want to sample uniformly and efficiently all these possible realizations. (This problem can be considered as a special case of Tutte's $f$-factor problem, however it has a favorable sampling speed.) We solve this {\em restricted degree sequence} (or RDS for short) problem completely if the forbidden edges form a bipartite graph, which consist of the union of a (not necessarily maximal) 1-factor and a (possible empty) star. Then we show how one can sample the space of all realizations of these RDSs uniformly and efficiently when the degree sequence describes a {\em half-regular} bipartite graph. Our result contains, as special cases, the well-known result of Kannan, Tetali and Vempala on sampling regular bipartite graphs and a recent result of Greenhill on sampling regular directed graphs (so it also provides new proofs of them). The RDS problem descried above is self-reducible, therefore our {\em fully polynomial almost uniform sampler} (a.k.a. FPAUS) on the space of all realizations also provides a {\em fully polynomial randomized approximation scheme} (a.k.a. FPRAS) for approximate counting of all realizations.

math.CO↗

Modulated String Searching

In his 1987 paper entitled "Generalized String Matching", Abrahamson introduced {\em pattern matching with character classes} and provided the first efficient algorithm to solve it. The best known solution to date is due to Linhart and Shamir (2009). Another broad yet comparatively less studied class of string matching problems is that of numerical string searching, such as, e.g., the `less-than' or $L_1$-norm string searching. The best known solutions for problems in this class are based on FFT convolution after some suitable re-encoding. The present paper introduces {\em modulated string searching} as a unified framework for string matching problems where the numerical conditions can be combined with some Boolean/numerical decision conditions on the character classes. One example problem in this class is the {\em locally bounded $L_1$-norm} matching problem on character classes: here the "match" between a character at some position in the text and a set of characters at some position in the pattern is assessed based on the smallest $L_1$ distance between the text character and one of those pattern characters. The two positions "match" if the (absolute value of the) difference between the two characters does not exceed a predefined constant. The pattern has an occurrence in an alignment with the text if the sum of all such differences does not exceed a second predefined constant value. This problem requires a pointwise evaluation of the quality of each match and has no known solution based on the previously mentioned algorithms.

cs.DS↗

Proving the Pressing Game Conjecture on Linear Graphs

The pressing game on black-and-white graphs is the following: Given a graph $G(V,E)$ with its vertices colored with black and white, any black vertex $v$ can be pressed, which has the following effect: (a) all neighbors of $v$ change color, i.e. white neighbors become black and \emph{vice versa}, (b) all pairs of neighbors of $v$ change connectivity, i.e. connected pairs become unconnected, unconnected ones become connected, (c) and finally, $v$ becomes a separated white vertex. The aim of the game is to transform $G$ into an all white, empty graph. It is a known result that the all white empty graph is reachable in the pressing game if each component of $G$ contains at least one black vertex, and for a fixed graph, any successful transformation has the same number of pressed vertices. The pressing game conjecture is that any successful pressing path can be transformed into any other successful pressing path with small alterations. Here we prove the conjecture for linear graphs. The connection to genome rearrangement and sorting signed permutations with reversals is also discussed.

cs.DM↗

On the swap-distances of different realizations of a graphical degree sequence

One of the first graph theoretical problems which got serious attention (already in the fifties of the last century) was to decide whether a given integer sequence is equal to the degree sequence of a simple graph (or it is {\em graphical} for short). One method to solve this problem is the greedy algorithm of Havel and Hakimi, which is based on the {\em swap} operation. Another, closely related question is to find a sequence of swap operations to transform one graphical realization into another one of the same degree sequence. This latter problem got particular emphases in connection of fast mixing Markov chain approaches to sample uniformly all possible realizations of a given degree sequence. (This becomes a matter of interest in connection of -- among others -- the study of large social networks.) Earlier there were only crude upper bounds on the shortest possible length of such swap sequences between two realizations. In this paper we develop formulae (Gallai-type identities) for these {\em swap-distance}s of any two realizations of simple undirected or directed degree sequences. These identities improves considerably the known upper bounds on the swap-distances.

math.CO↗

On Realizations of a Joint Degree Matrix

The joint degree matrix of a graph gives the number of edges between vertices of degree i and degree j for every pair (i,j). One can perform restricted swap operations to transform a graph into another with the same joint degree matrix. We prove that the space of all realizations of a given joint degree matrix over a fixed vertex set is connected via these restricted swap operations. This was claimed before, but there is an error in the previous proof, which we illustrate by example. We also give a simplified proof of the necessary and sufficient conditions for a matrix to be a joint degree matrix. Finally, we address some of the issues concerning the mixing time of the corresponding MCMC method to sample uniformly from these realizations.

math.CO↗

A simple Havel-Hakimi type algorithm to realize graphical degree sequences of directed graphs

One of the simplest ways to decide whether a given finite sequence of positive integers can arise as the degree sequence of a simple graph is the greedy algorithm of Havel and Hakimi. This note extends their approach to directed graphs. It also studies cases of some simple forbidden edge-sets. Finally, it proves a result which is useful to design an MCMC algorithm to find random realizations of prescribed directed degree sequences.

math.CO↗

Degree-based graph construction

Degree-based graph construction is an ubiquitous problem in network modeling, ranging from social sciences to chemical compounds and biochemical reaction networks in the cell. This problem includes existence, enumeration, exhaustive construction and sampling questions with aspects that are still open today. Here we give necessary and sufficient conditions for a sequence of nonnegative integers to be realized as a simple graph's degree sequence, such that a given (but otherwise arbitrary) set of connections from a arbitrarily given node are avoided. We then use this result to present a swap-free algorithm that builds {\em all} simple graphs realizing a given degree sequence. In a wider context, we show that our result provides a greedy construction method to build all the $f$-factor subgraphs embedded within $K_n\setminus S_k$, where $K_n$ is the complete graph and $S_k$ is a star graph centered on one of the nodes.

math.CO↗

Mathematical Framework for Phylogenetic Birth-And-Death Models

A phylogenetic birth-and-death model is a probabilistic graphical model for a so-called phylogenetic profile, i.e., the size distribution for a homolog gene family at the terminal nodes of a phylogeny. Profile datasets are used in bioinformatics analyses for the inference of evolutionary trees, and of functional associations between gene families, as well as for the quantification of various processes guiding genome evolution. Here we describe the mathematical formalism for phylogenetic birth-and-death models. We also present an algorithm for computing the likelihood in a gain-loss-duplication model.

q-bio.PE↗

A probabilistic model for gene content evolution with duplication, loss, and horizontal transfer

We introduce a Markov model for the evolution of a gene family along a phylogeny. The model includes parameters for the rates of horizontal gene transfer, gene duplication, and gene loss, in addition to branch lengths in the phylogeny. The likelihood for the changes in the size of a gene family across different organisms can be calculated in O(N+hM^2) time and O(N+M^2) space, where N is the number of organisms, $h$ is the height of the phylogeny, and M is the sum of family sizes. We apply the model to the evolution of gene content in Preoteobacteria using the gene families in the COG (Clusters of Orthologous Groups) database.

q-bio.PE↗