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  1. Dynamical processes running on different networks behave differently, which makes the reconstruction of the underlying network from dynamical observations possible. However, to what level of detail the network...

    Authors: Long Ma, Qiang Liu and Piet Van Mieghem
    Citation: Applied Network Science 2019 4:93
  2. Urban mobility data are important to areas ranging from traffic engineering to the analysis of outbreaks and disasters. In this paper, we study mobility data from a major Brazilian city from a geographical vie...

    Authors: Leonardo Bacelar Lima Santos, Luiz Max Carvalho, Wilson Seron, Flávio C. Coelho, Elbert E. Macau, Marcos G. Quiles and Antônio M. V. Monteiro
    Citation: Applied Network Science 2019 4:91
  3. During a single heartbeat, muscle cells in the heart contract and relax. Under healthy conditions, the behaviour of these muscle cells is almost identical from one beat to the next. However, this regular rhyth...

    Authors: Yi Ming Lai, Joshua Veasy, Stephen Coombes and Rüdiger Thul
    Citation: Applied Network Science 2019 4:90
  4. This paper details a network-based analysis of the spreading of rodent infestations through a city under varying conditions. Models of two very different cities, Tulsa, OK, USA and Providence, RI, USA, are cre...

    Authors: Dalton Brooks and John Matta
    Citation: Applied Network Science 2019 4:89
  5. The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent y...

    Authors: Saba A. Al-Sayouri, Danai Koutra, Evangelos E. Papalexakis and Sarah S. Lam
    Citation: Applied Network Science 2019 4:88
  6. We derive complexity estimates for two classes of deterministic networks: the Boolean networks S(Bn, m), which compute the Boolean vector-functions Bn, m, and the classes of graphs ...

    Authors: Alexander Goryashko, Leonid Samokhine and Pavel Bocharov
    Citation: Applied Network Science 2019 4:87
  7. Most real-world graphs collected from the Web like Web graphs and social network graphs are partially discovered or crawled. This leads to inaccurate estimates of graph properties based on link analysis such as P...

    Authors: Helge Holzmann, Avishek Anand and Megha Khosla
    Citation: Applied Network Science 2019 4:86
  8. The unveiling of communities within a network or graph, and the hierarchization of its members that results is of utmost importance in areas ranging from social to biochemical networks, from electronic circuit...

    Authors: Zineb Felfli, Roy George, Khalil Shujaee and Mohamed Kerwat
    Citation: Applied Network Science 2019 4:85

    The Correction to this article has been published in Applied Network Science 2020 5:43

  9. Nowadays, research has found a strong relationship between genomic status and occurrence of disease. Cancer is one of the most common diseases that leads to a high annual mortality rate worldwide, and the dise...

    Authors: Seyed Mohammad Razavi, Farzaneh Rami, Seyede Houri Razavi and Changiz Eslahchi
    Citation: Applied Network Science 2019 4:83
  10. A main challenge in mining network-based data is finding effective ways to represent or encode graph structures so that it can be efficiently exploited by machine learning algorithms. Several methods have focu...

    Authors: Leonardo Gutiérrez-Gómez and Jean-Charles Delvenne
    Citation: Applied Network Science 2019 4:82
  11. The existence of groups of nodes with common characteristics and the relationships between these groups are important factors influencing the structures of social, technological, biological, and other networks...

    Authors: Milos Kudelka, Eliska Ochodkova, Sarka Zehnalova and Jakub Plesnik
    Citation: Applied Network Science 2019 4:81
  12. Similarity measures are used extensively in machine learning and data science algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifyi...

    Authors: Sinan G. Aksoy, Kathleen E. Nowak, Emilie Purvine and Stephen J. Young
    Citation: Applied Network Science 2019 4:80
  13. This study examines the interface of three elements during co-contagion diffusion: the synergy between contagions, the dormancy rate of each individual contagion, and the multiplex network topology. Dormancy is d...

    Authors: Ho-Chun Herbert Chang and Feng Fu
    Citation: Applied Network Science 2019 4:78
  14. Recent neural networks designed to operate on graph-structured data have proven effective in many domains. These graph neural networks often diffuse information using the spatial structure of the graph. We pro...

    Authors: Stefan Dernbach, Arman Mohseni-Kabir, Siddharth Pal, Miles Gepner and Don Towsley
    Citation: Applied Network Science 2019 4:76
  15. Evaluating scientists based on their scientific production is a controversial topic. Nevertheless, bibliometrics and algorithmic approaches can assist traditional peer review in numerous tasks, such as attribu...

    Authors: Jorge Silva, David Aparício and Fernando Silva
    Citation: Applied Network Science 2019 4:74
  16. In this paper, we investigate the relationship between the coupling strengths and the extensive behaviour of the sum of the positive Lyapunov exponents of multiplex networks formed by coupled dynamical units. ...

    Authors: Maria Angélica Araujo and Murilo S. Baptista
    Citation: Applied Network Science 2019 4:73
  17. The friendship paradox is the observation that friends of individuals tend to have more friends or be more popular than the individuals themselves. In this work, we first study local metrics to capture the str...

    Authors: Siddharth Pal, Feng Yu, Yitzchak Novick, Ananthram Swami and Amotz Bar-Noy
    Citation: Applied Network Science 2019 4:71
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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

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