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Complex Networks IX

Proceedings of the 9th Conference on Complex Networks CompleNet 2018

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Sean Cornelius

    1. Center for Complex Network Research, Northeastern University, Boston, USA

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  2. Kate Coronges

    1. Network Science Institute, Northeastern University, Boston, USA

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  3. Bruno Gonçalves

    1. Center for Data Science, New York University, New York, USA

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  4. Roberta Sinatra

    1. Center for Network Science, and Department of Mathematics and its Applications, Central European University, Budapest, Hungary

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  5. Alessandro Vespignani

    1. Bouvé College of Health Sciences, Northeastern University, Boston, USA

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Part of the book series: Springer Proceedings in Complexity (SPCOM)

Included in the following conference series:

Conference proceedings info: CompleNet 2018.

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Table of contents (28 papers)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Theory of Complex Networks

    1. Front Matter

      Pages 1-1
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    2. On the Eccentricity Function in Graphs

      • Hend Alrasheed
      Pages 3-13

    3. Density Decompositions of Networks

      • Glencora Borradaile, Theresa Migler, Gordon Wilfong
      Pages 15-26

    4. Fast Streaming Small Graph Canonization

      • Pedro Paredes, Pedro Ribeiro
      Pages 27-40

    5. Silhouette for the Evaluation of Community Structures in Multiplex Networks

      • Alessia Amelio, Andrea Tagarelli
      Pages 41-49

    6. Jaccard Curvature—an Efficient Proxy for Ollivier-Ricci Curvature in Graphs

      • Siddharth Pal, Feng Yu, Terrence J. Moore, Ram Ramanathan, Amotz Bar-Noy, Ananthram Swami
      Pages 51-63

    7. Combinatorial Miller–Hagberg Algorithm for Randomization of Dense Networks

      • Hiroki Sayama
      Pages 65-73

    8. Proposal of Strategic Link Addition for Improving the Robustness of Multiplex Networks

      • Yui Kazawa, Sho Tsugawa
      Pages 75-84

  3. Graph Embeddings

    1. Front Matter

      Pages 85-85
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    2. Embedding-Centrality: Generic Centrality Computation Using Neural Networks

      • Rami Puzis, Zion Sofer, Dvir Cohen, Matan Hugi
      Pages 87-97

    3. Fast Sequence-Based Embedding with Diffusion Graphs

      • Benedek Rozemberczki, Rik Sarkar
      Pages 99-107

    4. Semi-supervised Graph Embedding Approach to Dynamic Link Prediction

      • Ryohei Hisano
      Pages 109-121

    5. Modularity Optimization as a Training Criterion for Graph Neural Networks

      • Tsuyoshi Murata, Naveed Afzal
      Pages 123-135

  4. Network Dynamics

    1. Front Matter

      Pages 137-137
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    2. Outer Synchronization for General Weighted Complex Dynamical Networks Considering Incomplete Measurements of Transmitted Information

      • Xinwei Wang, Guo-Ping Jiang, Xu Wu
      Pages 139-150

    3. Diffusive Phenomena in Dynamic Networks: A Data-Driven Study

      • Letizia Milli, Giulio Rossetti, Dino Pedreschi, Fosca Giannotti
      Pages 151-159

    4. Fractal Analyses of Networks of Integrate-and-Fire Stochastic Spiking Neurons

      • Ariadne A. Costa, Mary Jean Amon, Olaf Sporns, Luis H. Favela
      Pages 161-171

  5. Network Science Applications

    1. Front Matter

      Pages 173-173
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    2. Cultivating Tipping Points: Network Science in Teaching

      • Catherine Cramer, Ralucca Gera, Michaela Labriole, Hiroki Sayama, Lori Sheetz, Emma Towlson et al.
      Pages 175-183
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  1. Complex Networks IX

Keywords

About this book

This book aims to bring together researchers and practitioners working across domains and research disciplines to measure, model, and visualize complex networks. It collects the works presented at the 9th International Conference on Complex Networks (CompleNet) in Boston, MA, March, 2018. With roots in physical, information and social science, the study of complex networks provides a formal set of mathematical methods, computational tools and theories to describe, prescribe and predict dynamics and behaviors of complex systems. Despite their diversity, whether the systems are made up of physical, technological, informational, or social networks, they share many common organizing principles and thus can be studied with similar approaches. This book provides a view of the state-of-the-art in this dynamic field and covers topics such as group decision-making, brain and cellular connectivity, network controllability and resiliency, online activism, recommendation systems, and cyber security.

Editors and Affiliations

  • Center for Complex Network Research, Northeastern University, Boston, USA

    Sean Cornelius

  • Network Science Institute, Northeastern University, Boston, USA

    Kate Coronges

  • Center for Data Science, New York University, New York, USA

    Bruno Gonçalves

  • Center for Network Science, and Department of Mathematics and its Applications, Central European University, Budapest, Hungary

    Roberta Sinatra

  • BouvĂ© College of Health Sciences, Northeastern University, Boston, USA

    Alessandro Vespignani

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