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  1. The detection and prediction of risk in financial markets is one of the main challenges of economic forecasting, and draws much attention from the scientific community. An even more challenging task is the pre...

    Authors: Jingfang Fan, Keren Cohen, Louis M. Shekhtman, Sibo Liu, Jun Meng, Yoram Louzoun and Shlomo Havlin
    Citation: Applied Network Science 2019 4:69
  2. We propose sequence-to-sequence architectures for graph representation learning in both supervised and unsupervised regimes. Our methods use recurrent neural networks to encode and decode information from grap...

    Authors: Aynaz Taheri, Kevin Gimpel and Tanya Berger-Wolf
    Citation: Applied Network Science 2019 4:68
  3. In this paper, we present algorithms that learn and update temporal node embeddings on the fly for tracking and measuring node similarity over time in graph streams. Recently, several representation learning m...

    Authors: Ferenc Béres, Domokos M. Kelen, Róbert Pálovics and András A. Benczúr
    Citation: Applied Network Science 2019 4:64
  4. Information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, and biological networks. The primary challenge in...

    Authors: Mehmet E. Aktas, Esra Akbas and Ahmed El Fatmaoui
    Citation: Applied Network Science 2019 4:61
  5. This paper examines the process of protest claim-making by reconstructing the semantic structure of online communication that took place prior to the first street event of a protest. Topic networks are identif...

    Authors: Eunkyung Song
    Citation: Applied Network Science 2019 4:60
  6. PageRank for Semi-Supervised Learning has shown to leverage data structures and limited tagged examples to yield meaningful classification. Despite successes, classification performance can still be improved, ...

    Authors: Esteban Bautista, Patrice Abry and Paulo Gonçalves
    Citation: Applied Network Science 2019 4:57
  7. The availability of the entire Bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analysing the transaction graph to obtain insight on users behaviour. This pape...

    Authors: Damiano Di Francesco Maesa, Andrea Marino and Laura Ricci
    Citation: Applied Network Science 2019 4:56
  8. This study presents a novel approach to expand the emergent area of social bot research. We employ a methodological framework that aggregates and fuses data from multiple global Twitter conversations with an a...

    Authors: Ross Schuchard, Andrew T. Crooks, Anthony Stefanidis and Arie Croitoru
    Citation: Applied Network Science 2019 4:55
  9. The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circum...

    Authors: Natalie Stanley, Thomas Bonacci, Roland Kwitt, Marc Niethammer and Peter J. Mucha
    Citation: Applied Network Science 2019 4:54
  10. Random graph generators are necessary tools for many network science applications. For example, the evaluation of graph analysis algorithms requires methods for generating realistic synthetic graphs. Typically...

    Authors: Saskia Metzler and Pauli Miettinen
    Citation: Applied Network Science 2019 4:53
  11. We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering. In this paper, we provide experimental evidence to the claim that ECG alleviates the well...

    Authors: Valérie Poulin and François Théberge
    Citation: Applied Network Science 2019 4:51
  12. We investigate urban street networks as a whole within the frameworks of information physics and statistical physics. Urban street networks are envisaged as evolving social systems subject to a Boltzmann-mesos...

    Authors: Jérôme G. M. Benoit and Saif Eddin G. Jabari
    Citation: Applied Network Science 2019 4:49
  13. Political ideology is a major social phenomena that plays a crucial role in the formation and dynamics of ideologically-aligned social groups. This alignment gives rise to some of the most powerful social stru...

    Authors: Josemar Faustino, Hugo Barbosa, Eraldo Ribeiro and Ronaldo Menezes
    Citation: Applied Network Science 2019 4:48
  14. The study of network representations of physical, biological, and social phenomena can help us better understand their structure and functional dynamics as well as formulate predictive models of these phenomen...

    Authors: Varsha Chauhan, Alexander Gutfraind and Ilya Safro
    Citation: Applied Network Science 2019 4:46
  15. Local pattern mining on attributed networks is an important and interesting research area combining ideas from network analysis and data mining. In particular, local patterns on attributed networks allow both ...

    Authors: Martin Atzmueller, Henry Soldano, Guillaume Santini and Dominique Bouthinon
    Citation: Applied Network Science 2019 4:43
  16. Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In rea...

    Authors: Hana Khamfroush, Nathaniel Hudson, Samuel Iloo and Mahshid R. Naeini
    Citation: Applied Network Science 2019 4:40
  17. In the classic “influence-maximization” (IM) problem, people influence one another to adopt a product and the goal is to identify people to “seed” with the product so as to maximize long-term adoption. Many in...

    Authors: Shankar Iyer and Lada A. Adamic
    Citation: Applied Network Science 2019 4:38
  18. Applying closed pattern mining to attributed two-mode networks requires two conditions. First, as in two-mode networks there are two kinds of vertices, each described with a proper attribute set, we have to co...

    Authors: Henry Soldano, Guillaume Santini, Dominique Bouthinon, Sophie Bary and Emmanuel Lazega
    Citation: Applied Network Science 2019 4:37
  19. States facing the decision to develop a nuclear weapons program do so within a broader context of their relationships with other countries. How these diplomatic, economic, and strategic relationships impact pr...

    Authors: Bethany L. Goldblum, Andrew W. Reddie, Thomas C. Hickey, James E. Bevins, Sarah Laderman, Nathaniel Mahowald, Austin P. Wright, Elie Katzenson and Yara Mubarak
    Citation: Applied Network Science 2019 4:36
  20. The advent of Online Social Networks (OSNs) has offered the opportunity to study the dynamics of information spread and influence propagation at a huge scale. Considerable research has focused on the social in...

    Authors: Luca Luceri, Torsten Braun and Silvia Giordano
    Citation: Applied Network Science 2019 4:34
  21. Real network datasets provide significant benefits for understanding phenomena such as information diffusion or network evolution. Yet the privacy risks raised from sharing real graph datasets, even when strip...

    Authors: Sameera Horawalavithana, Juan Arroyo Flores, John Skvoretz and Adriana Iamnitchi
    Citation: Applied Network Science 2019 4:33
  22. We develop and test a rewiring method (originally proposed by Newman) which allows to build random networks having pre-assigned degree distribution and two-point correlations. For the case of scale-free degree...

    Authors: Maria Letizia Bertotti and Giovanni Modanese
    Citation: Applied Network Science 2019 4:32
  23. We present a model for network transformation mediated by confinement, as a demonstration of a simple network dynamics that has a direct connection with real world quantities. The model has the capacity of gen...

    Authors: Éder Mílton Schneider, Sebastián Gonçalves, José Roberto Iglesias and Bruno Requião da Cunha
    Citation: Applied Network Science 2019 4:30
  24. Community detection has proved to be extremely successful in a variety of domains. However, most of the algorithms used in practice assume networks are unchanging in time. This assumption is violated for many ...

    Authors: Thomas Magelinski and Kathleen M. Carley
    Citation: Applied Network Science 2019 4:25
  25. Networked power grid systems are susceptible to a phenomenon known as Coherent Swing Instability (CSI), in which a subset of machines in the grid lose synchrony with the rest of the network. We develop network...

    Authors: Daniel Dylewsky, Xiu Yang, Alexandre Tartakovsky and J. Nathan Kutz
    Citation: Applied Network Science 2019 4:24
  26. We study how the community structure of bipartite mutualistic networks changes in a dynamic context. First, we consider a real mutualistic network and introduce extinction events according to several scenarios...

    Authors: Somaye Sheykhali, Juan Fernández-Gracia, Anna Traveset and Víctor M. Eguíluz
    Citation: Applied Network Science 2019 4:23
  27. Transcriptional co-expression networks represent the concerted gene regulation programs by means of statistical inference of co-expression patterns. The rich phenomenology of transcriptional processes behind c...

    Authors: Guillermo de Anda-Jáuregui, Sergio Antonio Alcalá-Corona, Jesús Espinal-Enríquez and Enrique Hernández-Lemus
    Citation: Applied Network Science 2019 4:22
  28. Knowledge graph will be usefull for the intelligent system. As the relationship prediction on the knowledge graph becomes accurate, construction of a knowledge graph and detection of erroneous information incl...

    Authors: Yohei Onuki, Tsuyoshi Murata, Shun Nukui, Seiya Inagi, Xule Qiu, Masao Watanabe and Hiroshi Okamoto
    Citation: Applied Network Science 2019 4:20

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