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检索条件"主题词=Graph Algorithms"

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Very Sparse Additive Spanners and Emulators 15

Very Sparse Additive Spanners and Emulators

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6th Conference on Innovations in Theoretical Computer Science (ITCS)

作者： Bodwin, Gregory Williams, Virginia Vassilevska Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9781450333337

We obtain new upper bounds on the additive distortion for graph emulators and spanners on relatively few edges. We introduce a new subroutine called "strip creation," and we combine this subroutine with several other ideas to obtain the following results: 1. Every graph has a spanner on O(n(1+epsilon)) edges with (O) over tilde (n(1/2-epsilon/2)) additive distortion. 2. Every graph has an emulator on (O) over tilde (n(1+epsilon)) edges with (O) over tilde (n(1/3-2 epsilon/3)) additive distortion whenever epsilon is an element of [0, 1/5]. 3. Every graph has a spanner on (O) over tilde (n(1+epsilon)) edges with (O) over tilde (n(2/3-5 epsilon/3)) additive distortion whenever epsilon is an element of [0, 1/4]. Our first spanner has the new best known asymptotic edge-error tradeoff for additive spanners whenever epsilon is an element of [0, 1/7]. Our second spanner has the new best tradeoff whenever epsilon is an element of [1/7, 3/17]. Our emulator has the new best asymptotic edge-error tradeoff whenever epsilon is an element of [0, 1/5].

关键词： graph spanners graph algorithms graph emulators

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Provably Fast Inference of Latent Features from Networks with Applications to Learning Social Circles and Multilabel Classification 15

Provably Fast Inference of Latent Features from Networks wit...

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24th International Conference on World Wide Web (WWW)

作者： Tsourakakis, Charalampos E. Harvard Sch Engn & Appl Sci Cambridge MA 02138 USA

ISBN: (纸本)9781450334693

A well known phenomenon in social networks is homophily, the tendency of agents to connect with similar agents. A derivative of this phenomenon is the emergence of communities. Another phenomenon observed in numerous networks is the existence of certain agents that belong simultaneously to multiple communities. An understanding of these phenomena constitutes a central research topic of network science. In this work we focus on a fundamental theoretical question related to the above phenomena with various applications: given an undirected graph G, can we infer efficiently the latent vertex features which explain the observed network structure under the assumption of a generative model that exhibits homophily? We propose a probabilistic generative model with the property that the probability of an edge among two vertices is a non-decreasing function of the common features they possess. This property is true for many real-world networks and surprisingly is ignored by many popular overlapping community detection methods as it was shown recently by the empirical work of Yang and Leskovec [44]. Our main theoretical contribution is the first provably rapidly mixing Markov chain for inferring latent features. On the experimental side, we verify the efficiency of our method in terms of run times, where we observe that it significantly outperforms state-of-the-art methods. Our method is more than 2 400 times faster than a state-of-theart machine learning method [37] and typically provides nontrivial speedups compared to BigClam [43]. Furthermore, we verify on real-data with ground-truth available that our method learns efficiently high quality labelings. We use our method to learn social circles from Twitter ego-networks and perform multilabel classification.

关键词： Machine learning Markov chains graph algorithms Overlapping clustering Social circles

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A Constraint Programming Approach to the Cluster Deletion Problem 13

A Constraint Programming Approach to the Cluster Deletion Pr...

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13th Scandinavian Conference on Artificial Intelligence

作者： Mhamdi, Amel Naanaa, Wady Univ Sfax Dept Informat Sfax Tunisia Univ Monastir Dept Informat Monastir Tunisia

ISBN: (纸本)9781614995890;9781614995883

given a simple graph G = (V, E), the problem dealt with in this paper ask to transform a graph G by, only removing a minimum number of edges, into a disjoint union of cliques. This optimization problem is known to be NP-hard and is referred to as the Cluster Deletion problem (CD). This observation has motivated us to improve the chance for finding a disjoint union of cliques in a limited amount of search. To this end, we propose an encoding of CD in terms of a Weighted Constraint Satisfaction Problem (WCSP), a framework which has been widely used in solving hard combinatorial problems. We compare our approach with a fixed-parameter tractability FPT algorithm, which is one of the most used algorithm for solving the Cluster Deletion problem. Then, we experimentally show that the best results are obtained using the new encoding. We report a comparison of the quality and running times of WCSP and FPT algorithms on both random graphs and protein similarity graphs derived from the COG dataset.

关键词： Cluster deletion Fixed parameter tractability Cliques partition Constraint programming Limited discrepancy search Algorithm engineering graph algorithms

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Complex Network Analysis on Distributed Systems - An Empirical Comparison

Complex Network Analysis on Distributed Systems - An Empiric...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Koch, Jannis Staudt, Christian L. Vogel, Maximilian Meyerhenke, Henning Karlsruhe Inst Technol KIT Fac Informat Fasanengarten 5 D-76131 Karlsruhe Germany

ISBN: (纸本)9781450338547

Complex networks are relational data sets commonly represented as graphs. The analysis of their intricate structure is relevant to many areas of science and commerce, and data sets may reach sizes that require distributed storage and processing. We describe and compare programming models for distributed computing with a focus on graph algorithms for large-scale complex network analysis. Four frameworks - graphLab, Apache Giraph, Giraph++ and Apache Flink - are used to implement algorithms for the representative problems Connected Components, Community Detection, PageRank and Clustering Coefficients. The implementations are executed on a computer cluster to evaluate the frameworks' suitability in practice and to compare their performance to that of the single-machine, shared-memory parallel network analysis package NetworKit. Out of the distributed frameworks, graphLab and Apache Giraph generally show the best performance. In our experiments a cluster of eight computers running Apache Giraph enables the analysis of a network with about 2 billion edges, which is too large for a single machine of the same type. However, for networks that fit into memory of one machine, the performance of the shared-memory parallel implementation is far better than the distributed ones. The study provides experimental evidence for selecting the appropriate framework depending on the task and data volume.

关键词： distributed computing graph algorithms complex networks

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Boruvka-Incremental Power Greedy Heuristic for Strong Minimum Energy Topology in Wireless Sensor Networks 15

Boruvka-Incremental Power Greedy Heuristic for Strong Minimu...

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16th International Conference on Distributed Computing and Networking

作者： Panda, B. S. Bhatta, B. K. Mishra, Deepak De, Swades Indian Inst Technol Delhi Comp Sci & Applicat Grp Dept Math Hauz Khas New Delhi 110016 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

ISBN: (纸本)9781450329286

Given a set of sensors, the strong minimum energy topology (SMET) problem is to assign transmission range to each sensor node so that the sum of the transmission range for all the sensor is minimum subject to the constraint that the network is strongly connected (there is a directed path between every pair of nodes in the Network). This problem is known to be NP-hard. As this problem has lots of practical applications, several approximation algorithms and heuristics have been proposed. In this paper, we propose a new heuristic called Boruvka-incremental power greedy heuristic based on the Boruvka algorithm for the minimum spanning tree (MST) problem for solving the SMET problem. We compare the performance of the Boruvka-incremental power greedy heuristic with Kruskal-incremental power greedy heuristic and Prim-incremental power greedy heuristic. Extensive simulation results illustrate that Boruvka heuristic outperforms the Kruskal-incremental power greedy heuristic and Prim-incremental power greedy heuristic. We have also proved that apart from providing significant improvement in terms of average power savings, Boruvka incremental power greedy heuristic takes O(n) time for planar graphs as compared to O(n log n) time taken by Kruskal-incremental power greedy heuristic and O(n(2)) time taken by Prim-incremental power greedy heuristic, where n is the number of nodes in the network.

关键词： Wireless Sensor Networks Topology Control Minimum Spanning Tree graph algorithms Boruvka algorithm

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Global t-Sweep Coverage Problem with Minimum Interference 14

Global t-Sweep Coverage Problem with Minimum Interference

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2015 14th International Conference on Information Technology

作者： Bhatta, B. K. Jena, S. K. Indian Inst Technol Delhi Dept Math Comp Sci & Applicat Grp New Delhi 110016 India Utkal Univ Sch Math Stat Comp Sci Bhubaneswar 751004 Odisha India

ISBN: (纸本)9781509004874

Sweep coverage is one of the important and recent issue for network monitoring in wireless sensor network(WSN). Sweep coverage is efficiently applicable where periodic monitoring is sufficient than continuous monitoring. Interference minimization is another main objective of topology control problem in wireless sensor network. Reducing interference in turn reduces the number of collision and packet retransmission, which decreases the energy consumption and increases lifetime of the network. In this paper, we introduce the Interference minimization global t-Gsweep coverage problem(IMGtSCP) where the objective is to minimize the interference number induced by set of mobile and static sensors with the constraint that the given set of point of interests(PoIs) guarantee t-Gsweep coverage. We prove that the IMGtSCP problem is NP-hard and cannot be approximated with a factor 2. We propose a new heuristic called Interference Minimization Sweep Coverage with Static and Mobile Sensors. We have shown that the time complexity of this heuristics is O(n(3) log n), We also conduct simulation to show the performance of our proposed algorithm.

关键词： Wireless Sensor Networks Topology Control Minimum Spanning Tree graph algorithms Sweep Coverage k-MST Mobile Sensors Interference

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Compression-Aware algorithms for Massive Datasets

Compression-Aware Algorithms for Massive Datasets

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Data Compression Conference (DCC)

作者： Brunelle, Nathan Robins, Gabriel Shelat, Abhi Univ Virginia Dept Comp Sci Charlottesville VA 22904 USA

ISBN: (纸本)9781479984305

While massive datasets are often stored in compressed format, most algorithms are designed to operate on uncompressed data. We address this growing disconnect by developing a framework for compression-aware algorithms that operate directly on compressed datasets. Synergistically, we also propose new algorithmically-aware compression schemes that enable algorithms to efficiently process the compressed data. In particular, we apply this general methodology to geometric / CAD datasets that are ubiquitous in areas such as graphics, VLSI, and geographic information systems. We develop example algorithms and corresponding compression schemes that address different types of datasets, including point sets and graphs. Our methods are more efficient than their classical counterparts, and they extend to both lossless and lossy compression scenarios. This motivates further investigation of how this approach can enable algorithms to process ever-increasing big data volumes.

关键词： algorithmically-aware compressions compression-aware algorithms geometric algorithms graph algorithms graph compression pointset compression Geometric algorithms graph algorithms Dataset algorithms lossy compression COMPRESSED Lossless geographic information systems

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Distributed Programming over Time-series graphs 29

Distributed Programming over Time-series Graphs

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29th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Simmhan, Yogesh Choudhury, Neel Wickramaarachchi, Charith Kumbhare, Alok Frincu, Marc Raghavendra, Cauligi Prasanna, Viktor Indian Inst Sci Bangalore 560012 Karnataka India Univ Southern Calif Los Angeles CA 90089 USA

ISBN: (纸本)9781479986484

graphs are a key form of Big Data, and performing scalable analytics over them is invaluable to many domains. There is an emerging class of inter-connected data which accumulates or varies over time, and on which novel algorithms both over the network structure and across the time-variant attribute values is necessary. We formalize the notion of time-series graphs and propose a Temporally Iterative BSP programming abstraction to develop algorithms on such datasets using several design patterns. Our abstractions leverage a sub-graph centric programming model and extend it to the temporal dimension. We present three time-series graph algorithms based on these design patterns and abstractions, and analyze their performance using the GoFFish distributed platform on Amazon AWS Cloud. Our results demonstrate the efficacy of the abstractions to develop practical time-series graph algorithms, and scale them on commodity hardware.

关键词： Big Data platforms distributed systems graph algorithms time-series data

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Efficient Threshold for Volumetric Segmentation 7

Efficient Threshold for Volumetric Segmentation

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7th International Conference on Digital Image Processing (ICDIP)

作者： Burdescu, Dumitru Dan Brezovan, Marius Stanescu, Liana Stoica Spahiu, Cosmin Ebanca, Daniel Univ Craiova Computers & Informat Technol Dept Fac Automat Comp & Elect Craiova Dolj Romania

ISBN: (纸本)9781628418293

Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

关键词： Volumetric Segmentation graph-based segmentation graph algorithms Threshold Dissimilarity

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Efficient algorithms for Public-Private Social Networks 15

Efficient Algorithms for Public-Private Social Networks

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21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Chierichetti, Flavio Epasto, Alessandro Kumar, Ravi Lattanzi, Silvio Mirrokni, Vahab Sapienza Univ Rome Italy Brown Univ Providence RI 02912 USA Google Mountain View CA USA Google New York NY USA

ISBN: (纸本)9781450336642

We introduce the public-private model of graphs. In this model, we have a public graph and each node in the public graph has an associated private graph. The motivation for studying this model stems from social networks, where the nodes are the users, the public graph is visible to everyone, and the private graph at each node is visible only to the user at the node. From each node's viewpoint, the graph is just a union of its private graph and the public graph. We consider the problem of efficiently computing various properties of the graphs from each node's point of view, with minimal amount of recomputation on the public graph. To illustrate the richness of our model, we explore two powerful computational paradigms for studying large graphs, namely, sketching and sampling, and focus on some key problems in social networks and show efficient algorithms in the public private graph model. In the sketching model, we show how to efficiently approximate the neighborhood function, which in turn can be used to approximate various notions of centrality. In the sampling model, we focus on all-pair shortest path distances, node similarities, and correlation clustering.

关键词： Privacy Social Networks graph algorithms

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