Arboricity and Subgraph Listing Algorithms*
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Towards Maximum Independent Sets on Massive Graphs
Towards Maximum Independent Sets on Massive Graphs Yu Liuy Jiaheng Lu x Hua Yangy Xiaokui Xiaoz Zhewei Weiy yDEKE, MOE and School of Information, Renmin University of China x Department of Computer Science, University of Helsinki, Finland zSchool of Computer Engineering, Nanyang Technological University, Singapore ABSTRACT Maximum independent set (MIS) is a fundamental problem in graph theory and it has important applications in many areas such as so- cial network analysis, graphical information systems and coding theory. The problem is NP-hard, and there has been numerous s- tudies on its approximate solutions. While successful to a certain degree, the existing methods require memory space at least linear in the size of the input graph. This has become a serious concern in !"#$!%&'!(#&)*+,+)*+)-#.+- /"#$!%&'0'#&)*+,+)*+)-#.+- view of the massive volume of today’s fast-growing graphs. Figure 1: An example to illustrate that fv , v g is a maximal inde- In this paper, we study the MIS problem under the semi-external 1 2 pendent set, but fv , v , v , v g is a maximum independent set. setting, which assumes that the main memory can accommodate 2 3 4 5 all vertices of the graph but not all edges. We present a greedy algorithm and a general vertex-swap framework, which swaps ver- duced subgraphs, minimum vertex covers, graph coloring, and tices to incrementally increase the size of independent sets. Our maximum common edge subgraphs, etc. Its significance is not solutions require only few sequential scans of graphs on the disk just limited to graph theory but also in numerous real-world ap- file, thus enabling in-memory computation without costly random plications, such as indexing techniques for shortest path and dis- disk accesses. -
Matroids You Have Known
26 MATHEMATICS MAGAZINE Matroids You Have Known DAVID L. NEEL Seattle University Seattle, Washington 98122 [email protected] NANCY ANN NEUDAUER Pacific University Forest Grove, Oregon 97116 nancy@pacificu.edu Anyone who has worked with matroids has come away with the conviction that matroids are one of the richest and most useful ideas of our day. —Gian Carlo Rota [10] Why matroids? Have you noticed hidden connections between seemingly unrelated mathematical ideas? Strange that finding roots of polynomials can tell us important things about how to solve certain ordinary differential equations, or that computing a determinant would have anything to do with finding solutions to a linear system of equations. But this is one of the charming features of mathematics—that disparate objects share similar traits. Properties like independence appear in many contexts. Do you find independence everywhere you look? In 1933, three Harvard Junior Fellows unified this recurring theme in mathematics by defining a new mathematical object that they dubbed matroid [4]. Matroids are everywhere, if only we knew how to look. What led those junior-fellows to matroids? The same thing that will lead us: Ma- troids arise from shared behaviors of vector spaces and graphs. We explore this natural motivation for the matroid through two examples and consider how properties of in- dependence surface. We first consider the two matroids arising from these examples, and later introduce three more that are probably less familiar. Delving deeper, we can find matroids in arrangements of hyperplanes, configurations of points, and geometric lattices, if your tastes run in that direction. -
A New Spectral Bound on the Clique Number of Graphs
A New Spectral Bound on the Clique Number of Graphs Samuel Rota Bul`o and Marcello Pelillo Dipartimento di Informatica - University of Venice - Italy {srotabul,pelillo}@dsi.unive.it Abstract. Many computer vision and patter recognition problems are intimately related to the maximum clique problem. Due to the intractabil- ity of this problem, besides the development of heuristics, a research di- rection consists in trying to find good bounds on the clique number of graphs. This paper introduces a new spectral upper bound on the clique number of graphs, which is obtained by exploiting an invariance of a continuous characterization of the clique number of graphs introduced by Motzkin and Straus. Experimental results on random graphs show the superiority of our bounds over the standard literature. 1 Introduction Many problems in computer vision and pattern recognition can be formulated in terms of finding a completely connected subgraph (i.e. a clique) of a given graph, having largest cardinality. This is called the maximum clique problem (MCP). One popular approach to object recognition, for example, involves matching an input scene against a stored model, each being abstracted in terms of a relational structure [1,2,3,4], and this problem, in turn, can be conveniently transformed into the equivalent problem of finding a maximum clique of the corresponding association graph. This idea was pioneered by Ambler et. al. [5] and was later developed by Bolles and Cain [6] as part of their local-feature-focus method. Now, it has become a standard technique in computer vision, and has been employing in such diverse applications as stereo correspondence [7], point pattern matching [8], image sequence analysis [9]. -
Greedy Sequential Maximal Independent Set and Matching Are Parallel on Average
Greedy Sequential Maximal Independent Set and Matching are Parallel on Average Guy E. Blelloch Jeremy T. Fineman Julian Shun Carnegie Mellon University Georgetown University Carnegie Mellon University [email protected] jfi[email protected] [email protected] ABSTRACT edge represents the constraint that two tasks cannot run in parallel, the MIS finds a maximal set of tasks to run in parallel. Parallel al- The greedy sequential algorithm for maximal independent set (MIS) gorithms for the problem have been well studied [16, 17, 1, 12, 9, loops over the vertices in an arbitrary order adding a vertex to the 11, 10, 7, 4]. Luby’s randomized algorithm [17], for example, runs resulting set if and only if no previous neighboring vertex has been in O(log jV j) time on O(jEj) processors of a CRCW PRAM and added. In this loop, as in many sequential loops, each iterate will can be converted to run in linear work. The problem, however, is only depend on a subset of the previous iterates (i.e. knowing that that on a modest number of processors it is very hard for these par- any one of a vertex’s previous neighbors is in the MIS, or know- allel algorithms to outperform the very simple and fast sequential ing that it has no previous neighbors, is sufficient to decide its fate greedy algorithm. Furthermore the parallel algorithms give differ- one way or the other). This leads to a dependence structure among ent results than the sequential algorithm. This can be undesirable in the iterates. -
Fully Dynamic Maximal Independent Set with Polylogarithmic Update Time
2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS) Fully Dynamic Maximal Independent Set in Polylogarithmic Update Time Soheil Behnezhad∗, Mahsa Derakhshan∗, MohammadTaghi Hajiaghayi∗, Cliff Stein† and Madhu Sudan‡ ∗University of Maryland {soheil,mahsa,hajiagha}@cs.umd.edu †Columbia University [email protected] ‡Harvard University [email protected] Abstract— We present the first algorithm for maintaining a time. As such, one can trivially maintain MIS by recomput- maximal independent set (MIS) of a fully dynamic graph—which ing it from scratch after each update, in O(m) time. In a undergoes both edge insertions and deletions—in polylogarith- pioneering work, Censor-Hillel, Haramaty, and Karnin [15] mic time. Our algorithm is randomized and, per update, takes 2 2 O(log Δ · log n) expected time. Furthermore, the algorithm presented a round-efficient randomized algorithm for MIS in 2 4 can be adjusted to have O(log Δ · log n) worst-case update- dynamic distributed networks. Implementing the algorithm time with high probability. Here, n denotes the number of of [15] in the sequential setting—the focus of this paper— vertices and Δ is the maximum degree in the graph. requires Ω(Δ) update-time (see [15, Section 6]) where Δ The MIS problem in fully dynamic graphs has attracted sig- is the maximum-degree in the graph which can be as large nificant attention after a breakthrough result of Assadi, Onak, as Ω(n) or even Ω(m) for sparse graphs. Improving this Schieber, and Solomon [STOC’18] who presented an algorithm bound was one of the major problems the authors left 3/4 with O(m ) update-time (and thus broke the natural Ω(m) open. -
Minimum Dominating Set Approximation in Graphs of Bounded Arboricity
Minimum Dominating Set Approximation in Graphs of Bounded Arboricity Christoph Lenzen and Roger Wattenhofer Computer Engineering and Networks Laboratory (TIK) ETH Zurich {lenzen,wattenhofer}@tik.ee.ethz.ch Abstract. Since in general it is NP-hard to solve the minimum dominat- ing set problem even approximatively, a lot of work has been dedicated to central and distributed approximation algorithms on restricted graph classes. In this paper, we compromise between generality and efficiency by considering the problem on graphs of small arboricity a. These fam- ily includes, but is not limited to, graphs excluding fixed minors, such as planar graphs, graphs of (locally) bounded treewidth, or bounded genus. We give two viable distributed algorithms. Our first algorithm employs a forest decomposition, achieving a factor O(a2) approximation in randomized time O(log n). This algorithm can be transformed into a deterministic central routine computing a linear-time constant approxi- mation on a graph of bounded arboricity, without a priori knowledge on a. The second algorithm exhibits an approximation ratio of O(a log ∆), where ∆ is the maximum degree, but in turn is uniform and determinis- tic, and terminates after O(log ∆) rounds. A simple modification offers a trade-off between running time and approximation ratio, that is, for any parameter α ≥ 2, we can obtain an O(aα logα ∆)-approximation within O(logα ∆) rounds. 1 Introduction We are interested in the distributed complexity of the minimum dominating set (MDS) problem, a classic both in graph theory and distributed computing. Given a graph, a dominating set is a subset D of nodes such that each node in the graph is either in D, or has a direct neighbor in D. -