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Applied Network Science
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  1. 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

    Content type: Research

    Published on:

  2. 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

    Content type: Research

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  3. 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

    Content type: Research

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  6. 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

    Content type: Research

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  7. 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

    Content type: Research

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  8. 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

    Content type: Research

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  9. 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

    Content type: Research

    Published on:

  11. 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

    Content type: Research

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  13. 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

    Content type: Research

    Published on:

  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

    Content type: Research

    Published on:

  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

    Content type: Research

    Published on:

  18. 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

    Content type: Research

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  20. 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

    Content type: Research

    Published on:

  21. 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

    Content type: Research

    Published on:

  25. 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

    Content type: Research

    Published on:

  28. 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

    Content type: Review

    Published on:

  29. 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

    Content type: Research

    Published on:

  32. 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

    Content type: Research

    Published on:

  33. 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

    Content type: Research

    Published on:

  34. 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

    Content type: Research

    Published on:

  35. 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

    Content type: Research

    Published on:

  36. 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

    Content type: Research

    Published on:

  38. 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

    Content type: Research

    Published on:

  40. 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

    Content type: Research

    Published on:

  41. 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

    Content type: Research

    Published on:

  43. 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

    Content type: Research

    Published on:

  46. 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

    Content type: Research

    Published on:

  49. 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

    Content type: Research

    Published on:

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  • ISSN: 2364-8228 (electronic)

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