A note on compressed sensing and the complexity of matrix multiplication

We consider the conjectured O(N2+?) time complexity of multiplying any two N×N matrices A and B. Our main result is a deterministic Compressed Sensing (CS) algorithm that both rapidly and accurately computes A⋅B provided that the resulting matrix product is sparse/compressible. As a consequence of our main result we increase the class of matrices A, for any given N×N matrix B, which allows the exact computation of A⋅B to be carried out using the conjectured O(N2+?) operations. Additionally, in the process of developing our matrix multiplication procedure, we present a modified version of Indyk's recently proposed extractor-based CS algorithm [P. Indyk, Explicit constructions for compressed sensing of sparse signals, in: SODA, 2008] which is resilient to noise.     

On mimicking networks representing minimum terminal cuts

Given a capacitated undirected graph G=(V,E)$G=(V,E)$ with a set of terminals K⊂V$K\subset V$, a mimicking network   is a smaller graph H=(_VH,_EH)$H=(V_H,E_H)$ which contains the set of terminals K   and for every bipartition [U,K−U]$[U,K-U]$ of the terminals, the cost of the minimum cut separating U   from K−U$K-U$ in G is exactly equal to the cost of the minimum cut separating U   from K−U$K-U$ in H.     

社会网络中的影响力最大化问题

影响力最大化问题要求在网络中选取若干节点,使得以它们为初始节点进行信息传播时,在网络中产生的影响能够达到最大。影响力最大化问题是近十年来社会网络中的研究热点之一,其研究不仅具有理论意义,并且还具有应用前景。介绍了影响力最大化问题产生的背景,分析了问题的研究现状、研究用的几种主要传播模型以及解决问题的几种主要算法。最后,讨论了该研究面临的一些问题,对未来可能发展的研究方向进行了展望。     

A note on competitive diffusion through social networks

We introduce a game-theoretic model of diffusion of technologies, advertisements, or influence through a social network. The novelty in our model is that the players are interested parties outside the network. We study the relation between the diameter of the network and the existence of pure Nash equilibria in the game. In particular, we show that if the diameter is at most two then an equilibrium exists and can be found in polynomial time, whereas if the diameter is greater than two then an equilibrium is not guaranteed to exist.     

Simplified tight analysis of Johnson's algorithm

In their paper “Tight bound on Johnson's algorithm for maximum satisfiability” [J. Comput. System Sci. 58 (3) (1999) 622-640] Chen, Friesen and Zheng provided a tight bound on the approximation ratio of Johnson's algorithm for Maximum Satisfiability [J. Comput. System Sci. 9 (3) (1974) 256-278]. We give a simplified proof of their result and investigate to what extent it may be generalized to non-Boolean domains.     

A note on maximum independent set and related problems on box graphs

A box graph is the intersection graph of orthogonal rectangles in the plane. We show that maximum independent set and minimum vertex cover on box graphs can be solved in subexponential time, more precisely, in time , by applying Miller's simple cycle planar separator theorem [J. Comput. System Sci. 32 (1986) 265-279] (in spite of the fact that the input box graph might be strongly non-planar).     

Maximizing three-hop influence spread in social networks using discrete comprehensive learning artificial bee colony optimizer

Aiming at resolving the influence maximization (IM) problem in social networks, this paper proposes a three-layer-comprehensive-influence evaluation (TLCIE) model to measure the spread range of combinational nodes in the independent or weighted cascade models. The TLCIE is an enhanced three-hop influence spread model by integrating the intra- and inter-layer’s propagation effect, which improves the accuracy of propagation simulation and the reliability of parameter estimation. Then, an adaptive discrete artificial bee colony algorithm (ADABC) is devised to resolve the TLCIE model efficiently. In ADABC, the comprehensive-learning guided (CLG) updating rules, the degree-improvement initialization method and the semi-abandonment scout bee strategy are incorporated to enhance the search ability. Finally, the proposed model and algorithm are tested on a set of real-world social network instances, and the experimental results validate their effectiveness and efficiency.     

社交网络中对立影响最大化算法

已有社交网络影响力传播的研究工作主要关注单源信息传播情形,较少考虑对立的传播形式。针对对立影响最大化问题,扩展热量传播模型为多源热量传播模型,并提出一种预选式贪心近似（PSGA）算法。为验证算法有效性,选取7种具有代表性的种子挖掘方法,以对立影响最大化传播收益、算法运行时间及种子的富集程度为评价指标,在不同种类社会网络数据集上开展实验。结果表明,PSGA算法所选的种子传播能力更强,且密集程度低、表现稳定,在传播初期占据优势,可以认为PSGA算法能够解决对立影响最大化问题。     

A note on the query complexity of the Condorcet winner problem

Given an unknown tournament over {1,…,n}, we show that the query complexity of the question “Is there a vertex with outdegree n−1?” (known as a Condorcet winner in social choice theory) is exactly 2n−⌊log(n)⌋−2. This stands in stark contrast to the evasiveness of this property in general digraphs.     

A note on the greedy algorithm for the unsplittable flow problem

In a recent paper Chekuri and Khanna improved the analysis of the greedy algorithm for the edge disjoint paths problem and proved the same bounds also for the related uniform capacity unsplittable flow problem. Here we show that their ideas can be used to get the same approximation ratio even for the more general Unsplittable Flow Problem with nonuniform edge capacities.     

Scheduling time-constrained multicast messages in circuit-switched tree networks

In this paper, we consider a kind of multicast scheduling problem in a tree network. Each multicast message is transmitted through a directed subtree within the tree network. The transmission time of each multicast message is assumed to be one unit. Two messages can be transmitted at the same time if their subtrees are edge-disjoint. Each message is constrained by a ready time and a deadline, and has a weight we can gain if it is scheduled within its deadline. The optimality criterion is the total weight we gain. We assume that the degree of each subtree is bounded by a constant d and present an approximation algorithm of which the approximation ratio is at most 4d+15.     

A note on the approximation of mean-payoff games

We consider the problem of designing approximation schemes for the values of mean-payoff games. It was recently shown that (1) mean-payoff with rational weights scaled on [−1,1]$[-1,1]$ admit additive fully-polynomial approximation schemes, and (2) mean-payoff games with positive weights admit relative fully-polynomial approximation schemes. We show that the problem of designing additive/relative approximation schemes for general mean-payoff games (i.e. with no constraint on their edge-weights) is P-time equivalent to determining their exact solution.     

A tree-covering problem arising in integrity of tree-structured data

We introduce and solve a problem motivated by integrity verification in third-party data distribution: Given an undirected tree, find a minimum-cardinality set of simple paths that cover all the tree edges and, secondarily, have smallest total path lengths. We give a linear time algorithm for this problem.     

基于LT+模型的社交网络影响力最大化研究

影响力最大化问题的目标是寻找社交网络中一组种子结点集合,在给定的传播模型下,使得这些结点最终传播的影响范围最大。Kempe和Kleinberg提出的贪心算法可以获得很好的影响范围,但是因复杂度太高而并不适用于大型社交网络。Chen和Yuan等人基于线性阈值(LT)模型提出了构造局部有向无环图的启发式算法,但是LT模型只考虑了邻居结点的直接影响力,忽略了结点之间存在的间接影响力。因此,在LT模型的基础上,结合网络中结点之间存在的间接影响力,提出了LT⁺影响力模型,并利用构造局部有向无环图的启发式算法求解LT⁺模型的影响力最大化,称为LT⁺DAG算法。真实数据集上的对比实验表明,LT⁺DAG算法具有更好的影响范围以及较好的可扩展性。     

A note on improving the performance of approximation algorithms for radiation therapy

The segment minimization problem consists of representing an integer matrix as the sum of the fewest number of integer matrices each of which have the property that the non-zeroes in each row are consecutive. This has direct applications to an effective form of cancer treatment. Using several insights, we extend previous results to obtain constant-factor improvements in the approximation guarantees. We show that these improvements yield better performance by providing an experimental evaluation of all known approximation algorithms using both synthetic and real-world clinical data. Our algorithms are superior for 76% of instances and we argue for their utility alongside the heuristic approaches used in practice.     

A note on the online First-Fit algorithm for coloring k-inductive graphs

In a FOCS 1990 paper, S. Irani proved that the First-Fit online algorithm for coloring a graph uses at most O(klogn) colors for k-inductive graphs. In this note we provide a very short proof of this fact.     

A variant of the Ford-Johnson algorithm that is more space efficient

A variant of the Ford-Johnson or merge insertion sorting algorithm that we called four Ford-Johnson (₄FJ, for short) is presented and proved to execute exactly the same number of comparisons than the Ford-Johnson algorithm. The main advantage of our algorithm is that, instead of recursively working over lists of size the half of the input, as the Ford-Johnson algorithm does, ₄FJ recursively works over lists of size the quarter of the input. This allows for implementations of data structures for coordinating the recursive calls of size only 33% of the ones needed for the Ford-Johnson algorithm.     

A note on Rooted Survivable Networks

The (undirected) Rooted Survivable Network Design (Rooted SND) problem is: given a complete graph on node set V with edge-costs, a root sV, and (node-)connectivity requirements , find a minimum cost subgraph G that contains r(t) internally-disjoint st-paths for all tT. For large values of k=max_tTr(t) Rooted SND is at least as hard to approximate as Directed Steiner Tree [Y. Lando, Z. Nutov, Inapproximability of survivable networks, Theoret. Comput. Sci. 410 (21–23) (2009) 2122–2125]. For Rooted SND, [J. Chuzhoy, S. Khanna, Algorithms for single-source vertex-connectivity, in: FOCS, 2008, pp. 105–114] gave recently an approximation algorithm with ratio O(k²logn). Independently, and using different techniques, we obtained at the same time a simpler primal–dual algorithm with the same ratio.