Path Cover Problem Research Articles

Target control of linear directed networks based on the path cover problem

Securing complete control of complex systems comprised of tens of thousands of interconnected nodes holds immense significance across various fields, spanning from cell biology and brain science to human-engineered systems. However, depending on specific functional requirements, it can be more practical and efficient to focus on a pre-defined subset of nodes for control, a concept known as target control. While some methods have been proposed to find the smallest driver node set for target control, they either rely on heuristic approaches based on k-walk theory, lacking a guarantee of optimal solutions, or they are overly complex and challenging to implement in real-world networks. To address this challenge, we introduce a simple and elegant algorithm, inspired by the path cover problem, which efficiently identifies the nodes required to control a target node set within polynomial time. To practically apply the algorithm in real-world systems, we have selected several networks in which a specific set of nodes with functional significance can be designated as a target control set. The analysed systems include the complete connectome of the nematode worm C. elegans, the recently disclosed connectome of the Drosophila larval brain, as well as dozens of genome-wide metabolic networks spanning major plant lineages. The target control analysis shed light on distinctions between neural systems in nematode worms and larval brain insects, particularly concerning the number of nodes necessary to regulate specific functional systems. Furthermore, our analysis uncovers evolutionary trends within plant lineages, notably when examining the proportion of nodes required to control functional pathways.

Path Cover Problems with Length Cost

Path Cover Problems with Length Cost

Concatenated k-Path Covers

Given a graph , a k-Path Cover is defined as a subset C of the nodes V such that every simple path in G consisting of k nodes contains at least one node from C. Similarly, a k-Shortest Path Cover has to contain at least one node of every shortest path in G that consists of k nodes. In this paper, we extend the notion of k-(Shortest) Path Covers such that the objects to be covered don't have to be single paths but can be concatenations of up to p simple (or shortest) paths. For the generalized problem of computing concatenated -Shortest Path Covers, we present theoretical results regarding the VC-dimension of the concatenated path set in dependency of p in undirected as well as directed graphs. By proving a low VC-dimension in both settings, we enable the design of efficient approximation algorithms. Furthermore, we discuss how a pruning algorithm originally developed for k-Path Cover computation can be abstracted and modified in order to also solve concatenated -Path Cover problems. A crucial ingredient for the pruning algorithm to work efficiently is a path concatenation recognition algorithm. We describe general recognition algorithms for simple path concatenations as well as shortest path concatenations. Subsequently, we present more refined results for interesting special cases as piecewise shortest paths, hyperpaths, round tours, and trees. An extensive experimental study on different graph types proves the applicability and efficiency of our approaches.

Min-max coverage problems on tree-like metrics

We consider a number of min-max coverage problems. In each problem, the input is an unweighted graph G and an integer k, and possibly some additional information, such as a root vertex r. In the Min-Max Path Cover problem, the task is to cover all vertices of the graph by k walks, minimizing the length of the longest walk. The variant of Min-Max Path Cover in which all walks start and end at the same prescribed root vertex r is called the k-Traveling Salesmen Problem. In the Min-Max Tree Cover problem, the task is to cover all vertices of the graph by k trees, minimizing the size (number of edges) of the largest tree. In the rooted version, Min-Max k-Rooted Tree Cover, the input also contains k roots r1, ., rk, and the ith tree must contain the root ri. These four problems are all known to be APX-hard and to admit a constant-factor approximation. In this paper, we initiate the systematic study of these problems on trees and, more generally, on graphs of constant treewidth. As opposed to most graph problems, all four of the above coverage problems remain NP-hard even when G is a tree. We obtain an nO(k)-time exact algorithm for all four problems on graphs of bounded treewidth. Our main contribution is a quasi-polynomial-time approximation scheme (QPTAS) for the k-Traveling Salesmen Problem, Min-Max Path Cover, and Min-Max Tree Cover on graphs of bounded treewidth.

Algorithms for maximum internal spanning tree problem for some graph classes

For a given graph G, a maximum internal spanning tree of G is a spanning tree of G with maximum number of internal vertices. The Maximum Internal Spanning Tree (MIST) problem is to find a maximum internal spanning tree of the given graph. The MIST problem is a generalization of the Hamiltonian path problem. Since the Hamiltonian path problem is NP-hard, even for bipartite and chordal graphs, two important subclasses of graphs, the MIST problem also remains NP-hard for these graph classes. In this paper, we propose linear-time algorithms to compute a maximum internal spanning tree of cographs, block graphs, cactus graphs, chain graphs and bipartite permutation graphs. The optimal path cover problem, which asks to find a path cover of the given graph with maximum number of edges, is also a well studied problem. In this paper, we also study the relationship between the number of internal vertices in maximum internal spanning tree and number of edges in optimal path cover for the special graph classes mentioned above.

Computing directed Steiner path covers

In this article we consider the Directed Steiner Path Cover problem on directed co-graphs. Given a directed graph G=(V,E) and a set T subseteq V of so-called terminal vertices, the problem is to find a minimum number of vertex-disjoint simple directed paths, which contain all terminal vertices and a minimum number of non-terminal vertices (Steiner vertices). The primary minimization criteria is the number of paths. We show how to compute in linear time a minimum Steiner path cover for directed co-graphs. This leads to a linear time computation of an optimal directed Steiner path on directed co-graphs, if it exists. Since the Steiner path problem generalizes the Hamiltonian path problem, our results imply the first linear time algorithm for the directed Hamiltonian path problem on directed co-graphs. We also give binary integer programs for the (directed) Hamiltonian path problem, for the (directed) Steiner path problem, and for the (directed) Steiner path cover problem. These integer programs can be used to minimize change-over times in pick-and-place machines used by companies in electronic industry.

Simulated Annealing with Restart Strategy for the Path Cover Problem with Time Windows

This research presents a variant of the vehicle routing problem known as the path cover problem with time windows (PCPTW), in which each vehicle starts with a particular customer and finishes its route at another customer. The vehicles serve each customer within the customer’s time windows. PCPTW is motivated by a practical strategy for companies to reduce operational cost by hiring freelance workers, thus allowing workers to directly service customers without reporting to the office. A mathematical programming model is formulated for the problem. This research also proposes a simulated annealing heuristic with restart strategy (SARS) to solve PCPTW and test it on several benchmark datasets. Computational results indicate that the proposed SARS effectively solves PCPTW.

The Steiner cycle and path cover problem on interval graphs

The Steiner path problem is a common generalization of the Steiner tree and the Hamiltonian path problem, in which we have to decide if for a given graph there exists a path visiting a fixed set of terminals. In the Steiner cycle problem we look for a cycle visiting all terminals instead of a path. The Steiner path cover problem is an optimization variant of the Steiner path problem generalizing the path cover problem, in which one has to cover all terminals with a minimum number of paths. We study those problems for the special class of interval graphs. We present linear time algorithms for both the Steiner path cover problem and the Steiner cycle problem on interval graphs given as endpoint sorted lists. The main contribution is a lemma showing that backward steps to non-Steiner intervals are never necessary. Furthermore, we show how to integrate this modification to the deferred-query technique of Chang et al. to obtain the linear running times.

Target Controllability of Two-Layer Multiplex Networks Based on Network Flow Theory.

In this paper, we consider the target controllability of two-layer multiplex networks, which is an outstanding challenge faced in various real-world applications. We focus on a fundamental issue regarding how to allocate a minimum number of control sources to guarantee the controllability of each given target subset in each layer, where the external control sources are limited to interact with only one layer. It is shown that this issue is essentially a path cover problem, which is to locate a set of directed paths denoted as P and cycles denoted as C to cover the target sets under the constraint that the nodes in the second layer cannot be the starting node of any element in P , and the number of elements in P attains its minimum. In addition, the formulated path cover problem can be further converted into a maximum network flow problem, which can be efficiently solved by an algorithm called maximum flow-based target path-cover (MFTP). We rigorously prove that MFTP provides the minimum number of control sources for guaranteeing the target controllability of two-layer multiplex networks. It is anticipated that this paper would serve wide applications in target control of real-life networks.

On the isometric path partition problem

The isometric path cover (partition) problem of a graph is to find a minimum set of isometric paths which cover (partition) the vertex set of the graph. The isometric path cover (partition) number of a graph is the cardinality a minimum isometric path cover (partition). We prove that the isometric path partition problem and the isometric $k$-path partition problem for $k\geq 3$ are NP-complete on general graphs. Fisher and Fitzpatrick \cite{FiFi01} have shown that the isometric path cover number of $(r\times r)$-dimensional grid is $\lceil 2r/3\rceil$. We show that the isometric path cover (partition) number of $(r\times s)$-dimensional grid is $s$ when $r \geq s(s-1)$. We establish that the isometric path cover (partition) number of $(r\times r)$-dimensional torus is $r$ when $r$ is even and is either $r$ or $r+1$ when $r$ is odd. Then, we demonstrate that the isometric path cover (partition) number of an $r$-dimensional Benes network is $2^r$. In addition, we provide partial solutions for the isometric path cover (partition) problems for cylinder and multi-dimensional grids.

Corrigendum: LDFS-Based Certifying Algorithm for the Minimum Path Cover Problem on Cocomparability Graphs

This corrigendum corrects errors found by Jérémie Dusart in the proof of correctness of the algorithms in [D. G. Corneil, B. Dalton, and M. Habib, SIAM J. Comput., 42 (2013), pp. 792--807]; there are no changes in the algorithms themselves.

Counting primitive subsets and other statistics of the divisor graph of [formula omitted

Let Q(n) denote the count of the primitive subsets of the integers {1,2…,n}. We give a new proof that Q(n)=α(1+o(1))n for some constant α, which allows us to give a good error term and to improve upon the lower bound for the value of α. We also show that the method developed can be applied to many similar problems related to the divisor graph, including other questions about primitive sets, geometric-progression-free sets, and the divisor graph path-cover problem.

Target control and expandable target control of complex networks

Target control of complex networks, which aims to control only a target subset of network nodes instead of the entire network, is an outstanding challenge faced in various real world applications. Recently one fundamental issue regarding how to allocate a minimum number of control sources to guarantee the target controllability of a given target node set S was solved. This issue is shown to be essentially a path cover problem and it can be converted to a maximum network flow problem. In this work, we address another fundamental issue which is to further find the maximum (minimum) controllable node set to cover S using directed paths and circles based on the allocated minimum number of control sources. We show that such an issue can be solved by applying the maximum (minimum) cost maximum flow algorithm after introducing a cost for each edge in the flow network. Based on the obtained maximum (minimum) target controllable node set, we further propose a new index termed “expandable target controllability” to characterize complex networks that are expanding all the time. It is shown that “expandable target controllability” is an intrinsic property of various networks. We anticipate that this work would serve wide applications in target control and expandable target control of real-life networks.

Nontrivial path covers of graphs: existence, minimization and maximization

Let G be a graph and $${{{\mathcal {P}}}}$$ be a set of pairwise vertex-disjoint paths in G. We say that $${{\mathcal {P}}}$$ is a path cover if every vertex of G belongs to a path in $${{\mathcal {P}}}$$. In the minimum path cover problem, one wishes to find a path cover of minimum cardinality. In this problem, known to be $${\textsc {NP}}$$-hard, the set $${{\mathcal {P}}}$$ may contain trivial (single-vertex) paths. We study the problem of finding a path cover composed only of nontrivial paths. First, we show that the corresponding existence problem can be reduced to a matching problem. This reduction gives, in polynomial time, a certificate for both the yes-answer and the no-answer. When trivial paths are forbidden, for the feasible instances, one may consider either minimizing or maximizing the number of paths in the cover. We show that, the minimization problem on feasible instances is computationally equivalent to the minimum path cover problem: their optimum values coincide and they have the same approximation threshold. We show that the maximization problem can be solved in polynomial time. We also consider a weighted version of the path cover problem, in which we seek a path cover with minimum or maximum total weight (the number of paths do not matter), and we show that while the first is polynomial, the second is NP-hard, but admits a constant-factor approximation algorithm. We also describe a linear-time algorithm on (weighted) trees, and mention results for graphs with bounded treewidth.

The path cover problem: Formulation and a hybrid metaheuristic

This paper introduces the path cover problem (PCP) that is a variant of the capacitated vehicle routing problem (CVRP). In contrast to CVRP, a vehicle route in PCP involves a set of vertex-disjoint paths where a vehicle starts at a customer and finishes at another customer without traveling to the depot. PCP is defined to find a set of service paths to serve a set of customers that aiming to minimize the total traveling cost and hiring cost. This paper develops a hybrid approach that integrates variable neighborhood search (VNS) and tabu search (TS) to solve the problem. This algorithm presents an approach to explore the solution space by utilizing multi-set neighborhood and applying the systematic changing neighborhood of VNS. TS is incorporated into the algorithm to guide the search toward diverse regions. The computational results indicate that the proposed algorithm efficiently solves PCP.

Target Control of Directed Networks based on Network Flow Problems

Target control of directed networks, which aims to control only a target subset instead of the entire set of nodes in large natural and technological networks, is an outstanding challenge faced in various real world applications. We address one fundamental issue regarding this challenge, i.e., for a given target subset, how to allocate a minimum number of control sources which provide input signals to the network nodes. This issue remains open in general networks with loops. We show that the issue is essentially a path cover problem and can be further converted into a maximum network flow problem. A method termed `Maximum Flow based Target Path-cover' (MFTP) with complexity $O(|V|^{1/2}|E|)$ in which $|V|$ and $|E|$ denote the number of network nodes and edges is proposed. It is also rigorously proven to provide the minimum number of control sources on arbitrary directed networks, whether loops exist or not. We anticipate that this work would serve wide applications in target control of real-life networks, as well as counter control of various complex systems which may contribute to enhancing system robustness and resilience.

New LP relaxations for Minimum Cycle/Path/Tree Cover Problems

Given an undirected complete graph G=(V,E) with nonnegative edge weight function obeying the triangle inequality, a set {C1,C2, …,Ck} of cycles is called a cycle cover if V⊆⋃i=1kV(Ci), where V(Ci) represents the set of vertices in Ci, and its cost is given by the maximum weight of the cycles. The Minimum Cycle Cover Problem (MCCP) aims to find a cycle cover of cost at most λ with the minimum number of cycles. We propose new LP relaxations for MCCP as well as its variants, called the Minimum Path Cover Problem (MPCP) and the Minimum Tree Cover Problem, where the cycles are replaced by paths or trees. Moreover, we give new LP relaxations for a special case of the rooted version of MCCP/MPCP. We show that these LP relaxations have significantly better integrality gaps than the previous relaxations.

Sparse Dynamic Programming on DAGs with Small Width

The minimum path cover problem asks us to find a minimum-cardinality set of paths that cover all the nodes of a directed acyclic graph (DAG). We study the case when the size k of a minimum path cover is small, that is, when the DAG has a small width . This case is motivated by applications in pan-genomics , where the genomic variation of a population is expressed as a DAG. We observe that classical alignment algorithms exploiting sparse dynamic programming can be extended to the sequence-against-DAG case by mimicking the algorithm for sequences on each path of a minimum path cover and handling an evaluation order anomaly with reachability queries . Namely, we introduce a general framework for DAG-extensions of sparse dynamic programming. This framework produces algorithms that are slower than their counterparts on sequences only by a factor k . We illustrate this on two classical problems extended to DAGs: longest increasing subsequence and longest common subsequence . For the former, we obtain an algorithm with running time O ( k | E |log | V |). This matches the optimal solution to the classical problem variant when the input sequence is modeled as a path. We obtain an analogous result for the longest common subsequence problem. We then apply this technique to the co-linear chaining problem, which is a generalization of the above two problems. The algorithm for this problem turns out to be more involved, needing further ingredients, such as an FM-index tailored for large alphabets and a two-dimensional range search tree modified to support range maximum queries. We also study a general sequence-to-DAG alignment formulation that allows affine gap costs in the sequence. The main ingredient of the proposed framework is a new algorithm for finding a minimum path cover of a DAG ( V , E ) in O ( k | E |log | V |) time, improving all known time-bounds when k is small and the DAG is not too dense. In addition to boosting the sparse dynamic programming framework, an immediate consequence of this new minimum path cover algorithm is an improved space/time tradeoff for reachability queries in arbitrary directed graphs.

On the VC-dimension of unique round-trip shortest path systems

The VC-dimension, which has wide uses in learning theory, has been used in the analysis and design of graph algorithms recently. In this paper, we study the problem of bounding the VC-dimension of unique round-trip shortest path set systems (URTSP), which are set systems induced by sets of vertices in unique round-trip shortest paths in directed graphs. We first show that different from the VC-dimensions of set systems induced by unique undirected and directed shortest paths in undirected and directed graphs respectively, the VC-dimension of URTSP can be larger than 3. We then prove that the VC-dimension of URTSP is at most 32. Furthermore, we apply the VC-dimension result to the minimum k-round-trip shortest path cover problem (k-RTSPC), which is to find for a directed graph a minimum vertex set to intersect every round-trip shortest path containing at least k vertices, and derive an upper bound on the size of the vertex set. The k-RTSPC problem can be useful in many real-world applications, including optimal placement of facilities.

An improved approximation algorithm for the minimum 3-path partition problem

Given a graph $$G = (V, E)$$ , we seek for a collection of vertex disjoint paths each of order at most 3 that together cover all the vertices of V. The problem is called 3-path partition, and it has close relationships to the well-known path cover problem and the set cover problem. The general k-path partition problem for a constant $$k \ge 3$$ is NP-hard, and it admits a trivial k-approximation. When $$k = 3$$ , the previous best approximation ratio is 1.5 due to Monnot and Toulouse (Oper Res Lett 35:677–684, 2007), based on two maximum matchings. In this paper we first show how to compute in polynomial time a 3-path partition with the least 1-paths, and then apply a greedy approach to merge three 2-paths into two 3-paths whenever possible. Through an amortized analysis, we prove that the proposed algorithm is a 13 / 9-approximation. We also show that the performance ratio 13 / 9 is tight for our algorithm.