Complex Networks IX

1. Network Science Applications

   1. Terrorist Network Analyzed with an Influence Spreading Model

      • Vesa Kuikka

      Pages 185-197

   2. Author Attribution Using Network Motifs

      • Younis Al Rozz, Ronaldo Menezes

      Pages 199-207

   3. Complex Networks Reveal a Glottochronological Classification of Natural Languages

      • Harith Hamoodat, Younis Al Rozz, Ronaldo Menezes

      Pages 209-219

   4. A Percolation-Based Thresholding Method with Applications in Functional Connectivity Analysis

      • Farnaz Zamani Esfahlani, Hiroki Sayama

      Pages 221-231

   5. Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams

      • Tiphaine Viard, Raphaël Fournier-S’niehotta, Clémence Magnien, Matthieu Latapy

      Pages 233-241

   6. Router-Level Topologies of Autonomous Systems

      • Muhammed Abdullah Canbaz, Khalid Bakhshaliyev, Mehmet Hadi Gunes

      Pages 243-257

2. Human Behavior and Social Networks

   1. Front Matter

      Pages 259-259

      PDF

   2. Social Influence (Deep) Learning for Human Behavior Prediction

      • Luca Luceri, Torsten Braun, Silvia Giordano

      Pages 261-269

   3. Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation

      • Patrick Gildersleve, Taha Yasseri

      Pages 271-282

   4. Are Crisis Platforms Supporting Citizen Participation?

      • Gonzalo Bacigalupe, Javier Velasco-Martin

      Pages 283-290

   5. Dynamic Visualization of Citation Networks and Detection of Influential Node Addition

      • Takayasu Fushimi, Tetsuji Satoh, Noriko Kando

      Pages 291-302

   6. A Trust-Based News Spreading Model

      • V. Carchiolo, A. Longheu, M. Malgeri, G. Mangioni, M. Previti

      Pages 303-310

   7. Discovering Mobility Functional Areas: A Mobility Data Analysis Approach

      • Lorenzo Gabrielli, Daniele Fadda, Giulio Rossetti, Mirco Nanni, Leonardo Piccinini, Dino Pedreschi et al.

      Pages 311-322

   8. Estimating Peer-Influence Effects Under Homophily: Randomized Treatments and Insights

      • Niloy Biswas, Edoardo M. Airoldi

      Pages 323-347

3. Back Matter

   Pages 349-350

   PDF

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.

• complex networks
• social networks
• network science
• complexity science
• systems science
• group decision making
• brain and cellular activity
• network controllability
• computational social sciences
• complexity

• 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

• Bouvé College of Health Sciences, Northeastern University, Boston, USA

  Alessandro Vespignani

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