On the hardness of finding near-optimal multicuts in directed acyclic graphs

Given an edge-weighted (di)graph and a list of source-sink pairs of vertices of this graph, the minimum multicut problem consists in selecting a minimum-weight set of edges (or arcs), whose removal leaves no path from each source to the corresponding sink. This is a well-known NP-hard problem, and improving several previous results, we show that it remains APX-hard in unweighted directed acyclic graphs (DAG), even with only two source-sink pairs. This is also true if we remove vertices instead of arcs.     

Optimal Partitioning and Granularity of Uniform Task Graphs

Task partitioning is an important technique in parallel processing.In this paper,we investigate the optimal partitioning strategies and granularities of tasks with communications based on several models of parallel computer systems.Different from the usual approach,we study the optimal partitioning strategies and granularities from the viewpoint of minimizing T as well as minimizing NT^2,where N is the number of processors used and T is the program execution time using N processors.Our results show that the optimal partitioning strategies for all cases discussed in this paper are the same--either to assign all tasks to one processor or to distribute them among the processors as equally as possible depending only on the functions of ratio of running time to communication time R/C.     

Efficient coding of labeled directed acyclic graphs

 We use an adaptation of the Prüfer code for trees to encode labeled directed acyclic graphs, which are often abbreviated to DAGs (or ADGs). In this paper, each DAG is assigned a unique DAG code, which allows an easy handling for several purposes. The set of all possible DAG codes (and therefore the set of all DAGs) for a fixed number of n vertices can be generated efficiently. Furthermore, we are able to rank DAGs, i.e., we provide an algorithm that assigns every DAG a unique number in the set {0,…,a _n −1}, where a _n is the cardinality of the set of labeled DAGs with n≥1 vertices, and we are able to unrank DAGs, which is the inverse operation. We also gain recurrence relations, which can be used to calculate a _n and a _n,q , i.e., the number of DAGs with n vertices and q edges. Finally, it is possible to generate, enumerate, rank and unrank DAGs with given number of edges and also DAGs with bounded indegree. RID="*" ID="*" This research was supported by the Austrian Science Fund (FWF), P13261-INF. I want to thank the reviewers, specially the one who suggested to add the algorithm for unranking DAG codes, for reading the paper very carefully and for the helpful comments.     

Topological additive numbering of directed acyclic graphs

We propose to study a problem that arises naturally from both Topological Numbering of Directed Acyclic Graphs, and Additive Coloring (also known as Lucky Labeling). Let D be a digraph and f   a labeling of its vertices with positive integers; denote by S(v)$S(v)$ the sum of labels over all neighbors of each vertex v. The labeling f is called topological additive numbering   if S(u)<S(v)$S(u)<S(v)$ for each arc (u,v)$(u,v)$ of the digraph. The problem asks to find the minimum number k for which D   has a topological additive numbering with labels belonging to {1,…,k}$\{1,\dots ,k\}$, denoted by _ηt(D)${\eta }_t(D)$.     

Decomposition of structural learning about directed acyclic graphs

In this paper, we propose that structural learning of a directed acyclic graph can be decomposed into problems related to its decomposed subgraphs. The decomposition of structural learning requires conditional independencies, but it does not require that separators are complete undirected subgraphs. Domain or prior knowledge of conditional independencies can be utilized to facilitate the decomposition of structural learning. By decomposition, search for d-separators in a large network is localized to small subnetworks. Thus both the efficiency of structural learning and the power of conditional independence tests can be improved.     

Node overlap removal in clustered directed acyclic graphs

Graph drawing and visualization represent structural information as diagrams of abstract graphs and networks. An important subset of graphs is directed acyclic graphs (DAGs). This paper presents a new E-Spring algorithm, extended from the popular spring embedder model, which eliminates node overlaps in clustered DAGs. In this framework, nodes are modeled as non-uniform charged particles with weights, and a final drawing is derived by adjusting the positions of the nodes according to a combination of spring forces and repulsive forces derived from electrostatic forces between the nodes. The drawing process needs to reach a stable state when the average distances of separation between nodes are near optimal. We introduce a stopping condition for such a stable state, which reduces equilibrium distances between nodes and therefore results in a significantly reduced area for DAG visualization. It imposes an upper bound on the repulsive forces between nodes based on graph geometry. The algorithm employs node interleaving to eliminate any residual node overlaps. These new techniques have been validated by visualizing eBay buyer–seller relationships and has resulted in overall area reductions in the range of 45–79%.     

Processing directed acyclic graphs with recursive neural networks

Recursive neural networks are conceived for processing graphs and extend the well-known recurrent model for processing sequences. In Frasconi et al. (1998), recursive neural networks can deal only with directed ordered acyclic graphs (DOAGs), in which the children of any given node are ordered. While this assumption is reasonable in some applications, it introduces unnecessary constraints in others. In this paper, it is shown that the constraint on the ordering can be relaxed by using an appropriate weight sharing, that guarantees the independence of the network output with respect to the permutations of the arcs leaving from each node. The method can be used with graphs having low connectivity and, in particular, few outcoming arcs. Some theoretical properties of the proposed architecture are given. They guarantee that the approximation capabilities are maintained, despite the weight sharing.     

Recognition of directed acyclic graphs by spanning tree automata

In this paper, we study tree automata for directed acyclic graphs (DAGs). We define the movement of a tree automaton on a DAG so that a DAG is accepted by a tree automaton if and only if the DAG has a spanning tree accepted by the tree automaton. We call this automaton a spanning tree automaton. The NP-completeness of the membership problem of DAGs for spanning tree automata is shown. However, if inputs are restricted to series–parallel graphs or generalized series–parallel graphs, it is shown that the membership problem for spanning tree automata is solvable in linear time.     

Solving shortest paths efficiently on nearly acyclic directed graphs

Shortest path problems can be solved very efficiently when a directed graph is nearly acyclic. Earlier results defined a graph decomposition, now called the 1-dominator set, which consists of a unique collection of acyclic structures with each single acyclic structure dominated by a single associated trigger vertex. In this framework, a specialised shortest path algorithm only spends delete-min operations on trigger vertices, thereby making the computation of shortest paths through non-trigger vertices easier. A previously presented algorithm computed the 1-dominator set in O(mn) worst-case time, which allowed it to be integrated as part of an O(mn+nrlogr) time all-pairs algorithm. Here m and n respectively denote the number of edges and vertices in the graph, while r denotes the number of trigger vertices. A new algorithm presented in this paper computes the 1-dominator set in just O(m) time. This can be integrated as part of the O(m+rlogr) time spent solving single-source, improving on the value of r obtained by the earlier tree-decomposition single-source algorithm. In addition, a new bidirectional form of 1-dominator set is presented, which further improves the value of r by defining acyclic structures in both directions over edges in the graph. The bidirectional 1-dominator set can similarly be computed in O(m) time and included as part of the O(m+rlogr) time spent computing single-source. This paper also presents a new all-pairs algorithm under the more general framework where r is defined as the size of any predetermined feedback vertex set of the graph, improving the previous all-pairs time complexity from O(mn+nr²) to O(mn+r³).     

Performance analysis of parallel programs based on directed acyclic graphs

Task precedence graphs are widely used for modeling and evaluation of parallel applications. Their nodes represent the subtasks of the parallel program and the edges represent the precedence relations between the subtasks. The execution times of the subtasks are described by random variables and their distributions. In our paper we introduce a new class of distributions, particularly suited for the modeling and evaluation of parallel programs. Exponential polynomials introduced by Sahner and Trivedi have the disadvantage that a large number of parameters is needed for the representation of realistic task execution times, which usually have a small value of variation. We extend this class to derive the class of truncated -exponential polynomials which allow the representation of realistic task execution times with fewer parameters. Additionally this class of distributions has the advantage that minimum as well as maximum execution times can be guaranteed. Models with a large number of subtasks can not be evaluated on a computer using exact analytical methods because of memory requirements and numerical inaccuracies, which accumulate, when the operations of analysis are applied. Using extreme value theory we derive approximate formulas for the parallel independent execution of subtasks, a structure, which can be found in every parallel program. The obtained results for truncated and not truncated distributions show, that distributions with an infinite domain are not suitable, particularly for massively parallel structures. Received: 26 August 1994 / 13 May 1996     

有向无环图上k步可达查询优化算法

k步可达查询用于在给定的有向无环图（DAG）中回答两点之间是否存在长度不超过k的路径。针对现有方法的索引规模大、查询处理效率低的问题,提出一种基于部分点的双向最短路径索引来提升索引的可达信息覆盖率,并提出一组优化规则来减小索引规模;然后提出基于简化图的正反互逆拓扑索引来加速回答不可达查询;最后提出远距离优先的双向遍历策略来提高查询处理的效率。基于21个真实数据集（如引用网络、社交网络等）的实验结果表明,相比已有的高效方法PLL及BFSI-B,所提出的算法具有更小的索引规模和更快的查询响应速度。     

Collective motions and formations under pursuit strategies on directed acyclic graphs

A novel pursuit-based approach is presented to investigate collective motions and formations of a large number of agents with both single-integrator kinematics and double-integrator dynamics on directed acyclic graphs (DAGs). Each agent pursues its neighbors according to a directed acyclic graph, in which the agents without neighbors are leaders. Based on signal flow graph analysis and Mason’s rule, necessary and sufficient conditions are derived for BIBO stability of resulting pursuit systems. Moreover, achievable collective motions and formations are analyzed by adjusting a set of control parameters when leaders keep stationary, perform uniform rectilinear motions, and perform uniform circular motions. Finally, simulations are provided for achieving a static formation and mimicking several complex collective behaviors observed in nature, such as V-formation, vortex motions, and tornado motions.     

基于遗传算法的有向无环图画图算法

在研究了现有画有向无环图的主要方法的基础上提出一种基于遗传算法的有向无环图画图算法,将一般有向无环图的画图问题转换为函数优化问题,用遗传算法求目标函数最优解的近似值。实验表明此算法具有算法统一、方法简单、容易实现、易于修改,并且具有自适应、自学习和易于并行化的特点。     

On acyclic edge coloring of toroidal graphs

Let c be a proper edge coloring of a graph G. If there exists no bicolored cycle in G with respect to c, then c is called an acyclic edge coloring of G. Let G be a planar graph with maximum degree Δ and girth g. In Dong and Xu (2010) [8], Dong and Xu proved that G admits an acyclic edge coloring with Δ(G) colors if Δ?8 and g?7, or Δ?6 and g?8, or Δ?5 and g?9, or Δ?4 and g?10, or Δ?3 and g?14. In this note, we fix a small gap in the proof of Dong and Xu (2010) [8], and generalize the above results to toroidal graphs.