On the complete width and edge clique cover problems

A complete graph is the graph in which every two vertices are adjacent. For a graph \(G=(V,E)\), the complete width of G is the minimum k such that there exist k independent sets \(\mathtt {N}_i\subseteq V\), \(1\le i\le k\), such that the graph \(G'\) obtained from G by adding some new edges between certain vertices inside the sets \(\mathtt {N}_i\), \(1\le i\le k\), is a complete graph. The complete width problem is to decide whether the complete width of a given graph is at most k or not. In this paper we study the complete width problem. We show that the complete width problem is NP-complete on \(3K_2\)-free bipartite graphs and polynomially solvable on \(2K_2\)-free bipartite graphs and on \((2K_2,C_4)\)-free graphs. As a by-product, we obtain the following new results: the edge clique cover problem is NP-complete on \(\overline{3K_2}\)-free co-bipartite graphs and polynomially solvable on \(C_4\)-free co-bipartite graphs and on \((2K_2, C_4)\)-free graphs. We also give a characterization for k-probe complete graphs which implies that the complete width problem admits a kernel of at most \(2^k\) vertices. This provides another proof for the known fact that the edge clique cover problem admits a kernel of at most \(2^k\) vertices. Finally we determine all graphs of small complete width \(k\le 3\).     

A simple greedy approximation algorithm for the minimum connected $$$$-Center problem

In this paper, we consider the connected \(k\)-Center (\(CkC\)) problem, which can be seen as the classic \(k\)-Center problem with the constraint of internal connectedness, i.e., two nodes in a cluster are required to be connected by an internal path in the same cluster. \(CkC\) was first introduced by Ge et al. (ACM Trans Knowl Discov Data 2:7, 2008), in which they showed the \(NP\)-completeness for this problem and claimed a polynomial time approximation algorithm for it. However, the running time of their algorithm might not be polynomial, as one key step of their algorithm involves the computation of an \(NP\)-hard problem. We first present a simple polynomial time greedy-based \(2\)-approximation algorithm for the relaxation of \(CkC\)—the \(CkC^*\). Further, we give a \(6\)-approximation algorithm for \(CkC\).     

A simpler PTAS for connected <Emphasis Type="Italic">k</Emphasis>-path vertex cover in homogeneous wireless sensor network

Because of its application in the field of security in wireless sensor networks, k-path vertex cover (\(\hbox {VCP}_k\)) has received a lot of attention in recent years. Given a graph \(G=(V,E)\), a vertex set \(C\subseteq V\) is a k-path vertex cover (\(\hbox {VCP}_k\)) of G if every path on k vertices has at least one vertex in C, and C is a connected k-path vertex cover of G (\(\hbox {CVCP}_k\)) if furthermore the subgraph of G induced by C is connected. A homogeneous wireless sensor network can be modeled as a unit disk graph. This paper presents a new PTAS for \(\hbox {MinCVCP}_k\) on unit disk graphs. Compared with previous PTAS given by Liu et al., our method not only simplifies the algorithm and reduces the time-complexity, but also simplifies the analysis by a large amount.     

On computing a minimum secure dominating set in block graphs

In a graph \(G=(V,E)\), a set \(D \subseteq V\) is said to be a dominating set of G if for every vertex \(u\in V{\setminus }D\), there exists a vertex \(v\in D\) such that \(uv\in E\). A secure dominating set of the graph G is a dominating set D of G such that for every \(u\in V{\setminus }D\), there exists a vertex \(v\in D\) such that \(uv\in E\) and \((D{\setminus }\{v\})\cup \{u\}\) is a dominating set of G. Given a graph G and a positive integer k, the secure domination problem is to decide whether G has a secure dominating set of cardinality at most k. The secure domination problem has been shown to be NP-complete for chordal graphs via split graphs and for bipartite graphs. In Liu et al. (in: Proceedings of 27th workshop on combinatorial mathematics and computation theory, 2010), it is asked to find a polynomial time algorithm for computing a minimum secure dominating set in a block graph. In this paper, we answer this by presenting a linear time algorithm to compute a minimum secure dominating set in block graphs. We then strengthen the known NP-completeness of the secure domination problem by showing that the secure domination problem is NP-complete for undirected path graphs and chordal bipartite graphs.     

An improved parameterized algorithm for the <Emphasis Type="Italic">p</Emphasis>-cluster vertex deletion problem

In the p-Cluster Vertex Deletion problem, we are given a graph \(G=(V,E)\) and two parameters k and p, and the goal is to determine if there exists a subset X of at most k vertices such that the removal of X results in a graph consisting of exactly p disjoint maximal cliques. Let \(r=p/k\). In this paper, we design a branching algorithm with time complexity \(O(\alpha ^k+|V||E|)\), where \(\alpha \) depends on r and has a rough upper bound \(\min \{1.618^{1+r},2\}\). With a more precise analysis, we show that \(\alpha =1.28\cdot 3.57^{r}\) for \(r\le 0.219\); \(\alpha =(1-r)^{r-1}r^{-r}\) for \(0.219< r<1/2\); and \(\alpha =2\) for \(r\ge 1/2\), respectively. Our algorithm also works with the same time complexity for the variant that the number of clusters is at most p. Our result improves the previous best time complexity \(O^*(1.84^{p+k})\) and implies that for fixed p the problem can be solved as efficiently as Vertex Cover.     

OFDP: a distributed algorithm for finding disjoint paths with minimum total length in wireless sensor networks

This paper investigates the MINimum-length-\(k\)-Disjoint-Paths (MIN-\(k\)-DP) problem: in a sensor network, given two nodes \(s\) and \(t\), a positive integer \(k\), finding \(k\) (node) disjoint paths connecting \(s\) and \(t\) with minimum total length. An efficient distributed algorithm named Optimally-Finding-Disjoint-Paths (OFDP) is proposed for this problem. OFDP guarantees correctness and optimality, i.e., (1) it will find \(k\) disjoint paths if there exist \(k\) disjoint paths in the network or the maximum number of disjoint paths otherwise; (2) the disjoint paths it outputs do have minimum total length. To the best of our knowledge, OFDP is the first distributed algorithm that can solve the MIN-\(k\)-DP problem with correctness and optimality guarantee. Compared with the existing centralized algorithms which also guarantee correctness and optimality, OFDP is shown to be much more efficient by simulation results.     

Partial degree bounded edge pacing problem for graphs and $$$$-uniform hypergraphs

Given a graph \(G=(V,E)\) and a non-negative integer \(c_u\) for each \(u\in V\), partial degree bounded edge packing problem is to find a subgraph \(G^{\prime }=(V,E^{\prime })\) with maximum \(|E^{\prime }|\) such that for each edge \((u,v)\in E^{\prime }\), either \(deg_{G^{\prime }}(u)\le c_u\) or \(deg_{G^{\prime }}(v)\le c_v\). The problem has been shown to be NP-hard even for uniform degree constraint (i.e., all \(c_u\) being equal). In this work we study the general degree constraint case (arbitrary degree constraint for each vertex) and present two combinatorial approximation algorithms with approximation factors \(4\) and \(2\). Then we give a \(\log _2 n\) approximation algorithm for edge-weighted version of the problem and an efficient exact algorithm for edge-weighted trees with time complexity \(O(n\log n)\). We also consider a generalization of this problem to \(k\)-uniform hypergraphs and present a constant factor approximation algorithm based on linear programming using Lagrangian relaxation.     

$$$$-labeling number of Cartesian product of path and cycle

For positive numbers \(j\) and \(k\), an \(L(j,k)\)-labeling \(f\) of \(G\) is an assignment of numbers to vertices of \(G\) such that \(|f(u)-f(v)|\ge j\) if \(d(u,v)=1\), and \(|f(u)-f(v)|\ge k\) if \(d(u,v)=2\). The span of \(f\) is the difference between the maximum and the minimum numbers assigned by \(f\). The \(L(j,k)\)-labeling number of \(G\), denoted by \(\lambda _{j,k}(G)\), is the minimum span over all \(L(j,k)\)-labelings of \(G\). In this article, we completely determine the \(L(j,k)\)-labeling number (\(2j\le k\)) of the Cartesian product of path and cycle.     

Power domination with bounded time constraints

Based on the power observation rules, the problem of monitoring a power utility network can be transformed into the graph-theoretic power domination problem, which is an extension of the well-known domination problem. A set \(S\) is a power dominating set (PDS) of a graph \(G=(V,E)\) if every vertex \(v\) in \(V\) can be observed under the following two observation rules: (1) \(v\) is dominated by \(S\), i.e., \(v \in S\) or \(v\) has a neighbor in \(S\); and (2) one of \(v\)’s neighbors, say \(u\), and all of \(u\)’s neighbors, except \(v\), can be observed. The power domination problem involves finding a PDS with the minimum cardinality in a graph. Similar to message passing protocols, a PDS can be considered as a dominating set with propagation that applies the second rule iteratively. This study investigates a generalized power domination problem, which limits the number of propagation iterations to a given positive integer; that is, the second rule is applied synchronously with a bounded time constraint. To solve the problem in block graphs, we propose a linear time algorithm that uses a labeling approach. In addition, based on the concept of time constraints, we provide the first nontrivial lower bound for the power domination problem.     

On the vertex cover $$P_3$$ problem parameterized by treewidth

Consider a graph G. A subset of vertices, F, is called a vertex cover \(P_t\) (\(VCP_t\)) set if every path of order t contains at least one vertex in F. Finding a minimum \(VCP_t\) set in a graph is is NP-hard for any integer \(t\ge 2\) and is called the \(MVCP_3\) problem. In this paper, we study the parameterized algorithms for the \(MVCP_3\) problem when the underlying graph G is parameterized by the treewidth. Given an n-vertex graph together with its tree decomposition of width at most p, we present an algorithm running in time \(4^{p}\cdot n^{O(1)}\) for the \(MVCP_3\) problem. Moreover, we show that for the \(MVCP_3\) problem on planar graphs, there is a subexponential parameterized algorithm running in time \(2^{O(\sqrt{k})}\cdot n^{O(1)}\) where k is the size of the optimal solution.     

Distance domination in graphs with given minimum and maximum degree

For an integer \(k \ge 1\), a distance k-dominating set of a connected graph G is a set S of vertices of G such that every vertex of V(G) is at distance at most k from some vertex of S. The distance k-domination number \(\gamma _k(G)\) of G is the minimum cardinality of a distance k-dominating set of G. In this paper, we establish an upper bound on the distance k-domination number of a graph in terms of its order, minimum degree and maximum degree. We prove that for \(k \ge 2\), if G is a connected graph with minimum degree \(\delta \ge 2\) and maximum degree \(\Delta \) and of order \(n \ge \Delta + k - 1\), then \(\gamma _k(G) \le \frac{n + \delta - \Delta }{\delta + k - 1}\). This result improves existing known results.     

Algorithms for connected <Emphasis Type="Italic">p</Emphasis>-centdian problem on block graphs

We consider the facility location problem of locating a set \(X_p\) of p facilities (resources) on a network (or a graph) such that the subnetwork (or subgraph) induced by the selected set \(X_p\) is connected. Two problems on a block graph G are proposed: one problem is to minimizes the sum of its weighted distances from all vertices of G to \(X_p\), another problem is to minimize the maximum distance from each vertex that is not in \(X_p\) to \(X_p\) and, at the same time, to minimize the sum of its distances from all vertices of G to \(X_p\). We prove that the first problem is linearly solvable on block graphs with unit edge length. For the second problem, it is shown that the set of Pareto-optimal solutions of the two criteria has cardinality not greater than n, and can be obtained in \(O(n^2)\) time, where n is the number of vertices of the block graph G.     

Neighbor sum distinguishing list total coloring of subcubic graphs

Let \(G=(V, E)\) be a simple graph and denote the set of edges incident to a vertex v by E(v). The neighbor sum distinguishing (NSD) total choice number of G, denoted by \(\mathrm{ch}_{\Sigma }^{t}(G)\), is the smallest integer k such that, after assigning each \(z\in V\cup E\) a set L(z) of k real numbers, G has a total coloring \(\phi \) satisfying \(\phi (z)\in L(z)\) for each \(z\in V\cup E\) and \(\sum _{z\in E(u)\cup \{u\}}\phi (z)\ne \sum _{z\in E(v)\cup \{v\}}\phi (z)\) for each \(uv\in E\). In this paper, we propose some reducible configurations of NSD list total coloring for general graphs by applying the Combinatorial Nullstellensatz. As an application, we present that \(\mathrm{ch}^{t}_{\Sigma }(G)\le \Delta (G)+3\) for every subcubic graph G.     

2-Edge connected dominating sets and 2-Connected dominating sets of a graph

A \(k\)-connected (resp. \(k\)-edge connected) dominating set \(D\) of a connected graph \(G\) is a subset of \(V(G)\) such that \(G[D]\) is \(k\)-connected (resp. \(k\)-edge connected) and each \(v\in V(G)\backslash D\) has at least one neighbor in \(D\). The \(k\) -connected domination number (resp. \(k\) -edge connected domination number) of a graph \(G\) is the minimum size of a \(k\)-connected (resp. \(k\)-edge connected) dominating set of \(G\), and denoted by \(\gamma _k(G)\) (resp. \(\gamma '_k(G)\)). In this paper, we investigate the relation of independence number and 2-connected (resp. 2-edge-connected) domination number, and prove that for a graph \(G\), if it is \(2\)-edge connected, then \(\gamma '_2(G)\le 4\alpha (G)-1\), and it is \(2\)-connected, then \(\gamma _2(G)\le 6\alpha (G)-3\), where \(\alpha (G)\) is the independent number of \(G\).     

Improved approximations for buy-at-bul and shallow-light $$$$-Steiner trees and $$(,2)$$(,2)-subgraph

In this paper we give improved approximation algorithms for some network design problems. In the bounded-diameter or shallow-light \(k\)-Steiner tree problem (SL\(k\)ST), we are given an undirected graph \(G=(V,E)\) with terminals \(T\subseteq V\) containing a root \(r\in T\), a cost function \(c:E\rightarrow \mathbb {R}^+\), a length function \(\ell :E\rightarrow \mathbb {R}^+\), a bound \(L>0\) and an integer \(k\ge 1\). The goal is to find a minimum \(c\)-cost \(r\)-rooted Steiner tree containing at least \(k\) terminals whose diameter under \(\ell \) metric is at most \(L\). The input to the buy-at-bulk \(k\)-Steiner tree problem (BB\(k\)ST) is similar: graph \(G=(V,E)\), terminals \(T\subseteq V\) containing a root \(r\in T\), cost and length functions \(c,\ell :E\rightarrow \mathbb {R}^+\), and an integer \(k\ge 1\). The goal is to find a minimum total cost \(r\)-rooted Steiner tree \(H\) containing at least \(k\) terminals, where the cost of each edge \(e\) is \(c(e)+\ell (e)\cdot f(e)\) where \(f(e)\) denotes the number of terminals whose path to root in \(H\) contains edge \(e\). We present a bicriteria \((O(\log ^2 n),O(\log n))\)-approximation for SL\(k\)ST: the algorithm finds a \(k\)-Steiner tree with cost at most \(O(\log ^2 n\cdot \text{ opt }^*)\) where \(\text{ opt }^*\) is the cost of an LP relaxation of the problem and diameter at most \(O(L\cdot \log n)\). This improves on the algorithm of Hajiaghayi et al. (2009) (APPROX’06/Algorithmica’09) which had ratio \((O(\log ^4 n), O(\log ^2 n))\). Using this, we obtain an \(O(\log ^3 n)\)-approximation for BB\(k\)ST, which improves upon the \(O(\log ^4 n)\)-approximation of Hajiaghayi et al. (2009). We also consider the problem of finding a minimum cost \(2\)-edge-connected subgraph with at least \(k\) vertices, which is introduced as the \((k,2)\)-subgraph problem in Lau et al. (2009) (STOC’07/SICOMP09). This generalizes some well-studied classical problems such as the \(k\)-MST and the minimum cost \(2\)-edge-connected subgraph problems. We give an \(O(\log n)\)-approximation algorithm for this problem which improves upon the \(O(\log ^2 n)\)-approximation algorithm of Lau et al. (2009).     

Improved upper bound for the degenerate and star chromatic numbers of graphs

Let \(G=G(V,E)\) be a graph. A proper coloring of G is a function \(f:V\rightarrow N\) such that \(f(x)\ne f(y)\) for every edge \(xy\in E\). A proper coloring of a graph G such that for every \(k\ge 1\), the union of any k color classes induces a \((k-1)\)-degenerate subgraph is called a degenerate coloring; a proper coloring of a graph with no two-colored \(P_{4}\) is called a star coloring. If a coloring is both degenerate and star, then we call it a degenerate star coloring of graph. The corresponding chromatic number is denoted as \(\chi _{sd}(G)\). In this paper, we employ entropy compression method to obtain a new upper bound \(\chi _{sd}(G)\le \lceil \frac{19}{6}\Delta ^{\frac{3}{2}}+5\Delta \rceil \) for general graph G.     

An $$O^{*}(1.4366^n)$$-time exact algorithm for maximum $$P_2$$P2-packing in cubic graphs

Given a graph \(G=(V, E)\), a \(P_2\)-packing \(\mathcal {P}\) is a collection of vertex disjoint copies of \(P_2\)s in \(G\) where a \(P_2\) is a simple path with three vertices and two edges. The Maximum \(P_2\)-Packing problem is to find a \(P_2\)-packing \(\mathcal {P}\) in the input graph \(G\) of maximum cardinality. This problem is NP-hard for cubic graphs. In this paper, we give a branch-and-reduce algorithm for the Maximum \(P_2\)-Packing problem in cubic graphs. We analyze the running time of the algorithm using measure-and-conquer and show that it runs in time \(O^{*}(1.4366^n)\) which is faster than previous known exact algorithms where \(n\) is the number of vertices in the input graph.     

A quadratic time exact algorithm for continuous connected 2-facility location problem in trees

This paper studies the continuous connected 2-facility location problem (CC2FLP) in trees. Let \(T = (V, E, c, d, \ell , \mu )\) be an undirected rooted tree, where each node \(v \in V\) has a weight \(d(v) \ge 0\) denoting the demand amount of v as well as a weight \(\ell (v) \ge 0\) denoting the cost of opening a facility at v, and each edge \(e \in E\) has a weight \(c(e) \ge 0\) denoting the cost on e and is associated with a function \(\mu (e,t) \ge 0\) denoting the cost of opening a facility at a point x(e, t) on e where t is a continuous variable on e. Given a subset \(\mathcal {D} \subseteq V\) of clients, and a subset \(\mathcal {F} \subseteq \mathcal {P}(T)\) of continuum points admitting facilities where \(\mathcal {P}(T)\) is the set of all the points on edges of T, when two facilities are installed at a pair of continuum points \(x_1\) and \(x_2\) in \(\mathcal {F}\), the total cost involved in CC2FLP includes three parts: the cost of opening two facilities at \(x_1\) and \(x_2\), K times the cost of connecting \(x_1\) and \(x_2\), and the cost of all the clients in \(\mathcal {D}\) connecting to some facility. The objective is to open two facilities at a pair of continuum points in \(\mathcal {F}\) to minimize the total cost, for a given input parameter \(K \ge 1\). This paper focuses on the case of \(\mathcal {D} = V\) and \(\mathcal {F} = \mathcal {P}(T)\). We first study the discrete version of CC2FLP, named the discrete connected 2-facility location problem (DC2FLP), where two facilities are restricted to the nodes of T, and devise a quadratic time edge-splitting algorithm for DC2FLP. Furthermore, we prove that CC2FLP is almost equivalent to DC2FLP in trees, and develop a quadratic time exact algorithm based on the edge-splitting algorithm. Finally, we adapt our algorithms to the general case of \(\mathcal {D} \subseteq V\) and \(\mathcal {F} \subseteq \mathcal {P}(T)\).     

Parameterized algorithms for min–max 2-cluster editing

For a given graph and an integer t, the Min–Max 2-Clustering problem asks if there exists a modification of a given graph into two maximal disjoint cliques by inserting or deleting edges such that the number of the editing edges incident to each vertex is at most t. It has been shown that the problem can be solved in polynomial time for \(t<n/4\), where n is the number of vertices. In this paper, we design parameterized algorithms for different ranges of t. Let \(k=t-n/4\). We show that the problem is polynomial-time solvable when roughly \(k<\sqrt{n/32}\). When \(k\in o(n)\), we design a randomized and a deterministic algorithm with sub-exponential time parameterized complexity, i.e., the problem is in SUBEPT. We also show that the problem can be solved in \(O({2}^{n/r}\cdot n^2)\) time for \(k<n/12\) and in \(O(n^2\cdot 2^{3n/4+k})\) time for \(n/12\le k< n/4\), where \(r=2+\lfloor (n/4-3k-2)/(2k+1) \rfloor \ge 2\).     

Efficient approximation algorithms for computing <Emphasis Type="Italic">k</Emphasis> disjoint constrained shortest paths

Let \(G=(V,\, E)\) be a given directed graph in which every edge e is associated with two nonnegative costs: a weight w(e) and a length l(e). For a pair of specified distinct vertices \(s,\, t\in V\), the k-(edge) disjoint constrained shortest path (kCSP) problem is to compute k (edge) disjoint paths between s and t, such that the total length of the paths is minimized and the weight is bounded by a given weight budget \(W\in \mathbb {R}_{0}^{+}\). The problem is known to be \({\mathcal {NP}}\)-hard, even when \(k=1\) (Garey and Johnson in Computers and intractability, 1979). Approximation algorithms with bifactor ratio \(\left( 1\,+\,\frac{1}{r},\, r\left( 1\,+\,\frac{2(\log r\,+\,1)}{r}\right) (1\,+\,\epsilon )\right) \) and \((1\,+\,\frac{1}{r},\,1\,+\,r)\) have been developed for \(k=2\) in Orda and Sprintson (IEEE INFOCOM, pp. 727–738, 2004) and Chao and Hong (IEICE Trans Inf Syst 90(2):465–472, 2007), respectively. For general k, an approximation algorithm with ratio \((1,\, O(\ln n))\) has been developed for a weaker version of kCSP, the k bi-constraint path problem which is to compute k disjoint st-paths satisfying a given length constraint and a weight constraint simultaneously (Guo et al. in COCOON, pp. 325–336, 2013). This paper first gives an approximation algorithm with bifactor ratio \((2,\,2)\) for kCSP using the LP-rounding technique. The algorithm is then improved by adopting a more sophisticated method to round edges. It is shown that for any solution output by the improved algorithm, there exists a real number \(0\le \alpha \le 2\) such that the weight and the length of the solution are bounded by \(\alpha \) times and \(2-\alpha \) times of that of an optimum solution, respectively. The key observation of the ratio proof is to show that the fractional edges, in a basic solution against the proposed linear relaxation of kCSP, exactly compose a graph in which the degree of every vertex is exactly two. At last, by a novel enhancement of the technique in Guo et al. (COCOON, pp. 325–336, 2013), the approximation ratio is further improved to \((1,\,\ln n)\).