A heuristic relaxed extrapolated algorithm for accelerating PageRank

The PageRank algorithm for determining the importance of Web pages has become a central technique in Web search. This algorithm uses the Power method to compute successive iterates that converge to the principal eigenvector of the Markov chain representing the Web link graph. In this work we present an effective heuristic Relaxed and Extrapolated algorithm based on the Power method that accelerates its convergence. A hybrid parallel implementation of this algorithm has been designed by combining various OpenMP threads for each MPI process and several strategies of data distribution among nodes have been analyzed. The results show that the proposed algorithm can significantly speed up the convergence time with respect to the parallel Power algorithm.This research was partially supported by the Spanish Ministry of Science and Innovation under Grant Number TIN2011-26254 and Grant Number TIN2015-66972-C5-4-R, and by the European Union FEDER (CAPAP-H5 network TIN2014-53522-REDT)

Non-Stationary Acceleration Strategies for PageRank Computing

In this work, a non-stationary technique based on the Power method for accelerating the parallel computation of the PageRank vector is proposed and its theoretical convergence analyzed. This iterative non-stationary model, which uses the eigenvector formulation of the PageRank problem, reduces the needed computations for obtaining the PageRank vector by eliminating synchronization points among processes, in such a way that, at each iteration of the Power method, the block of iterate vector assigned to each process can be locally updated more than once, before performing a global synchronization. The parallel implementation of several strategies combining this novel non-stationary approach and the extrapolation methods has been developed using hybrid MPI/OpenMP programming. The experiments have been carried out on a cluster made up of 12 nodes, each one equipped with two Intel Xeon hexacore processors. The behaviour of the proposed parallel algorithms has been studied with realistic datasets, highlighting their performance compared with other parallel techniques for solving the PageRank problem. Concretely, the experimental results show a time reduction of up to 58.4% in relation to the parallel Power method, when a small number of local updates is performed before each global synchronization, outperforming both the two-stage algorithms and the extrapolation algorithms, more sharply as the number of processes increases.This research was supported by the Spanish Ministry of Science, Innovation and Universities Grant RTI2018-098156-B-C54, co-financed by the European Commission (FEDER funds)

Combining the Two-Layers PageRank Approach with the APA Centrality in Networks with Data

[EN] Identifying the influential nodes in complex networks is a fundamental and practical topic at the moment. In this paper, a new centrality measure for complex networks is proposed based on two contrasting models that have their common origin in the well-known PageRank centrality. On the one hand, the essence of the model proposed is taken from the Adapted PageRank Algorithm (APA) centrality, whose main characteristic is that constitutes a measure to establish a ranking of nodes considering the importance of some dataset associated to the network. On the other hand, a technique known as two-layers PageRank approach is applied to this model. This technique focuses on the idea that the PageRank centrality can be understood as a two-layer network, the topological and teleportation layers, respectively. The main point of the proposed centrality is that it combines the APA centrality with the idea of two-layers; however, the difference now is that the teleportation layer is replaced by a layer that collects the data present in the network. This combination gives rise to a new algorithm for ranking the nodes according to their importance. Subsequently, the coherence of the new measure is demonstrated by calculating the correlation and the quantitative differences of both centralities (APA and the new centrality). A detailed study of the differences of both centralities, taking different types of networks, is performed. A real urban network with data randomly generated is evaluated as well as the well-known Zachary's karate club network. Some numerical results are carried out by varying the values of the alpha parameter-known as dumping factor in PageRank model-that varies the importance given to the two layers (topology and data) within the computation of the new centrality. The proposed algorithm takes the best characteristics of the models on which it is based: on the one hand, it is a measure of centrality, in complex networks with data, whose calculation is stable numerically and, on the other hand, it is able to separate the topological properties of the network and the influence of the data.Partially supported by the Spanish Government, Ministerio de Economia y Competividad, grant number TIN2017-84821-P.Agryzkov, T.; Pedroche Sánchez, F.; Tortosa, L.; Vicent, JF. (2018). Combining the Two-Layers PageRank Approach with the APA Centrality in Networks with Data. ISPRS International Journal of Geo-Information. 7(12):1-22. https://doi.org/10.3390/ijgi7120480S122712Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3). doi:10.1103/physreve.73.036125Bonacich, P. (1991). Simultaneous group and individual centralities. Social Networks, 13(2), 155-168. doi:10.1016/0378-8733(91)90018-oStephenson, K., & Zelen, M. (1989). Rethinking centrality: Methods and examples. Social Networks, 11(1), 1-37. doi:10.1016/0378-8733(89)90016-6Szell, M., Lambiotte, R., & Thurner, S. (2010). Multirelational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences, 107(31), 13636-13641. doi:10.1073/pnas.1004008107Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., & Ratti, C. (2011). Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome. IEEE Transactions on Intelligent Transportation Systems, 12(1), 141-151. doi:10.1109/tits.2010.2074196Chen, D., Lü, L., Shang, M.-S., Zhang, Y.-C., & Zhou, T. (2012). Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 391(4), 1777-1787. doi:10.1016/j.physa.2011.09.017Zhou, Y.-B., Lü, L., & Li, M. (2012). Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity. New Journal of Physics, 14(3), 033033. doi:10.1088/1367-2630/14/3/033033Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and its Applications, 369(2), 853-866. doi:10.1016/j.physa.2005.12.063Jiang, B. (2009). Ranking spaces for predicting human movement in an urban environment. International Journal of Geographical Information Science, 23(7), 823-837. doi:10.1080/13658810802022822Bonacich, P. (1987). Power and Centrality: A Family of Measures. American Journal of Sociology, 92(5), 1170-1182. doi:10.1086/228631Boldi, P., & Vigna, S. (2014). Axioms for Centrality. Internet Mathematics, 10(3-4), 222-262. doi:10.1080/15427951.2013.865686Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35. doi:10.2307/3033543Brandes, U. (2001). A faster algorithm for betweenness centrality*. The Journal of Mathematical Sociology, 25(2), 163-177. doi:10.1080/0022250x.2001.9990249Haveliwala, T. H. (2003). Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering, 15(4), 784-796. doi:10.1109/tkde.2003.1208999Berkhin, P. (2005). A Survey on PageRank Computing. Internet Mathematics, 2(1), 73-120. doi:10.1080/15427951.2005.10129098García, E., Pedroche, F., & Romance, M. (2013). On the localization of the personalized PageRank of complex networks. Linear Algebra and its Applications, 439(3), 640-652. doi:10.1016/j.laa.2012.10.051Langville, A., & Meyer, C. (2004). Deeper Inside PageRank. Internet Mathematics, 1(3), 335-380. doi:10.1080/15427951.2004.10129091Bianchini, M., Gori, M., & Scarselli, F. (2005). Inside PageRank. ACM Transactions on Internet Technology, 5(1), 92-128. doi:10.1145/1052934.1052938Migallón, H., Migallón, V., Palomino, J. A., & Penadés, J. (2018). A heuristic relaxed extrapolated algorithm for accelerating PageRank. Advances in Engineering Software, 120, 88-95. doi:10.1016/j.advengsoft.2016.01.024Agryzkov, T., Oliver, J. L., Tortosa, L., & Vicent, J. F. (2012). An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector. Applied Mathematics and Computation, 219(4), 2186-2193. doi:10.1016/j.amc.2012.08.064Agryzkov, T., Tortosa, L., & Vicent, J. F. (2016). New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks. Applied Mathematics and Computation, 291, 14-29. doi:10.1016/j.amc.2016.06.036Agryzkov, T., Tortosa, L., Vicent, J. F., & Wilson, R. (2017). A centrality measure for urban networks based on the eigenvector centrality concept. Environment and Planning B: Urban Analytics and City Science, 46(4), 668-689. doi:10.1177/2399808317724444Conti, M., & Kumar, M. (2010). Opportunities in Opportunistic Computing. Computer, 43(1), 42-50. doi:10.1109/mc.2010.19Zhang, Y., Song, L., Jiang, C., Tran, N. H., Dawy, Z., & Han, Z. (2017). A Social-Aware Framework for Efficient Information Dissemination in Wireless Ad Hoc Networks. IEEE Communications Magazine, 55(1), 174-179. doi:10.1109/mcom.2017.1600029cmBoccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., … Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1-122. doi:10.1016/j.physrep.2014.07.001Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203-271. doi:10.1093/comnet/cnu016Pedroche, F., Romance, M., & Criado, R. (2016). A biplex approach to PageRank centrality: From classic to multiplex networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(6), 065301. doi:10.1063/1.4952955Zachary, W. W. (1977). An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropological Research, 33(4), 452-473. doi:10.1086/jar.33.4.362975

Open Problems in (Hyper)Graph Decomposition

Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final application concerns a different problem (such as traversal, finding paths, trees, and flows), decomposing large graphs is often an important subproblem for complexity reduction or parallelization. This report is a summary of discussions that happened at Dagstuhl seminar 23331 on "Recent Trends in Graph Decomposition" and presents currently open problems and future directions in the area of (hyper)graph decomposition

2nd International Conference on Numerical and Symbolic Computation

The Organizing Committee of SYMCOMP2015 – 2nd International Conference on Numerical and Symbolic Computation: Developments and Applications welcomes all the participants and acknowledge the contribution of the authors to the success of this event. This Second International Conference on Numerical and Symbolic Computation, is promoted by APMTAC - Associação Portuguesa de Mecânica Teórica, Aplicada e Computacional and it was organized in the context of IDMEC/IST - Instituto de Engenharia Mecânica. With this ECCOMAS Thematic Conference it is intended to bring together academic and scientific communities that are involved with Numerical and Symbolic Computation in the most various scientific area

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Appreciative Inquiry summits and organizational knowledge creation: A social systems perspective

The purpose of this exploratory study is to develop alternative models for analyzing the systems dynamics of a large group conference format called appreciative inquiry (AI) summits. I apply Luhmann’s social systems theory to the strategizing activities of AI summits to examine how this particular format is capable of generating organizational knowledge. An AI summit is a strategic planning conference in which hundreds of internal and external stakeholders collectively design the future of the organization through structured activities. It applies the principles of AI, a consulting method used in organizational development that attends to the positive aspects of an organization as opposed to its problems. Critics challenge this unconditional focus on the positive, questioning the validity of its methods and techniques. Indeed, very few rigorous evaluations of AI methods including AI summits exist. I propose a new approach for assessing the effectiveness of AI summits. I focus on knowledge creation as the dependent variable. Previous studies have shown that successful AI interventions generate new knowledge, not just transformational change. I conceptualize an AI summit as a strategic episode that allows an organization to temporarily suspend its routines and structures for strategic reflection. According to social systems theory, organizations are autopoietic (self-reproducing) systems that maintain their identity through an ongoing production of decision communications. An AI summit consists of three different types of systems that co-evolve and are structurally coupled: an organization system, interaction system and the individual participants’ psychological systems. I propose a typology for analyzing episodes during an AI summit as a starting point for determining the structural dynamics inherent in an AI summit system. Using illustrative examples from a case study, I identify five structural features of an AI summit that facilitate organizational knowledge creation, including reduced communication barriers and the production of decisions during the conference. The study contributes to the existing literature by identifying the important but understudied role of self-organizing project teams in the knowledge creation process at an AI summit. Limitations and implications are discussed

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project