26 papers · ranked by Valyu relevance
James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz + 4 more
Networks are often categorized according to the underlying phenomena that they represent, such as re-tweets, protein interactions, or web page links. It is generally believed that networks from different categories have inherently unique network characteristics. In this work, we find strong evidence supporting this…
Ian Barnett, Nishant Malik, Marieke L. Kuijjer, Peter J. Mucha + 1 more
'Jukka‐Pekka Onnela'] Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural building blocks [1]. These features tend to be shared by networks belonging to the same broad class, such as the class…
Benoît Pichon, Rémy Le Goff, Hélène Morlon, Benoît Perez-Lamarque
Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology, and conservation. A highly debated question is whether and how the structure of a bipartite ecological network…
Kansuke Ikehara, Aaron Clauset
The structure of complex networks has been of interest in many scientific and engineering disciplines over the decades. A number of studies in the field have been focused on finding the common properties among different kinds of networks such as heavy-tail degree distribution, small-worldness and modular structure and…
Mengbo Li, Daniel Kessler, Jesús Arroyo, Saskia Freytag + 3 more
The investigation of brain networks has yielded many insights that have helped to characterise many neurological and psychiatric disorders. In particular, network classification of functional magnetic resonance imaging (fMRI) data is an important tool for the identification of prognostic and diagnostic biomarkers of…
Mahdi Rabbani, Yongli Wang, Reza Khoshkangini, Hamed Jelodar + 6 more
'Ruxin Zhao' 'Sajjad Bagheri Baba Ahmadi' 'Seyedvalyallah Ayobi' 'Eirik Rosnes' 'Alexandre Graell i Amat' 'Hsuan-Yin Lin'] Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive…
Md. Mehedi Hassan Onik, Nasr Al-Zaben, Hung Phan Hoo, Chul‐Soo Kim
With the development of incipient technologies, user devices becoming more exposed and ill-used by foes. In upcoming decades, traditional security measures will not be sufficient enough to handle this huge threat towards distributed hardware and software. Lack of standard network attack taxonomy has become an…
Heewon Park, Satoru Miyano, Senthilnathan Palaniyandi
Unraveling the genetic regulatory networks that underlie diseases is essential for comprehending the intricate mechanisms of these conditions. While various computational strategies were developed, the approaches in the existing studies concerning network-based prediction and classification are based on the…
Ruyue Xin, Jiang Zhang, Yitong Shao
Classifying large-scale networks into several categories and distinguishing them according to their ne structures is of great importance with several applications in real life. However, most studies of complex networks focus on properties of a single network but seldom on classication, clustering, and comparison…
Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia + 1 more
'Alessandro Garavaglia' 'Nelly Litvak'] We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen…
Weiguang Liu, Rafael Delalibera Rodrigues, Jianglong Yan, Yu-tao Zhu + 5 more
'Everson José de Freitas Pereira' 'Gen Li' 'Qiusheng Zheng' 'Liang Zhao' 'Zhaoqing Pan'] In this work, we present a network-based technique for chest X-ray image classification to help the diagnosis and prognosis of patients with COVID-19. From visual inspection, we perceive that healthy and COVID-19 chest radiographic…
Harrison B. Smith, Hyunju Kim, Sara I. Walker
Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological…
Insuk Lee, Eiru Kim, Edward M. Marcotte
We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Consequently, a major dichotomy in previously studied networks arises from modeling networks in…
Jian Shen, Jingbo Xia, Shufu Dong, Xiaoyan Zhang + 2 more
'Chenping Hou'] Traffic identification of the target category is currently a significant challenge for network monitoring and management. To identify the target category with pertinence, a feature extraction algorithm based on the subset with highest proportion is presented in this paper. The method is proposed to be…
Enrico Borriello
The search for motifs –recurrent patterns in the topology of a network– has allowed the identification of universal classes of complex systems from very diverse fields, and has been used as a quantitative tool to highlight unifying properties of evolved and designed networks. This work explores if, and to what extent…
Kyumars Sheykh Esmaili, Jaideep Chandrashekar, Pascal Le Guyadec
We study the problem of inferring the type of a networked device in a home network by leveraging low level traffic activity indicators seen at commodity home gateways. We analyze a dataset of detailed device network activity obtained from 240 subscriber homes of a large European ISP and extract a number of traffic and…
Alicja Miniak-Górecka, Krzysztof Podlaski, Tomasz Gwizdałła, Yilun Shang
'Yilun Shang'] The classification of multi-dimensional patterns is one of the most popular and often most challenging problems of machine learning. That is why some new approaches are being tried, expected to improve existing ones. The article proposes a new technique based on the decision network called…
Yang Yu, Shuang Wang, Dong Xu, Juexin Wang
The spatial arrangement of cells within tissues plays a pivotal role in shaping tissue functions. A critical spatial pattern is network motif as the building blocks of cell organization. Network motifs can be represented as recurring significant interconnections of cells with various types in a spatial cell-relation…
Authors not listed
Because topology plays a key role in many chemical and physical properties of materials, identification of topology from crystalline structures is a common and important task in materials science. We present here a new web application, CrystalNets, whose user-friendly interface allows scientists to identify and…
JL Caldu-Primo, ER Alvarez-Buylla, J Davila-Velderrain
Network biology aims to understand cell behavior through the analysis of underlying complex biomolecular networks. Inference of condition-specific interaction networks from epigenomic data enables the characterization of the structural plasticity that regulatory networks can acquire in different tissues of the same…
Jana Weber, Pietro Lio’, Alexei Lapkin
Networks of chemical reactions represent relationships between molecules within chemical supply chains and promise to enhance planning of multi-step synthesis routes from bio-renewable feedstocks. This study aims to identify strategic molecules in chemical reaction networks that may potentially play a significant role…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here we report new methods of CRN generation and analysis that overcome…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Aniruddh Khanwale + 3 more
Chemical reaction networks (CRNs) are powerful tools for obtaining insight into complex reactive processes. However, they are difficult to employ when reaction mechanisms and products are not thoroughly understood. Here we report new methods of CRN generation and analysis that seek to overcome these limitations. We…
Chi Zhang, Dmytro Antypov, Matthew J Rosseinsky, Matthew Stephen Dyer
Machine learning has found wide application in the materials field, particularly in discovering structure-property relationships. However, its potential in predicting synthetic accessibility of materials remains relatively unexplored due to the lack of negative data. In this study, we employ several one-class…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these…
Jonathan Fine, Anand Rasjashekar, Krupal P. Jethava, Gaurav Chopra
State-of-the-art identification of the functional groups present in an unknown chemical entity requires expertise of a skilled spectroscopist to analyse and interpret Fourier Transform Infra-Red (FTIR), Mass Spectroscopy (MS) and/or Nuclear Magnetic Resonance (NMR) data. This process can be time-consuming and…