From the bridges of 17th century Königsberg to the internet, network science emerged from a diverse range of fields, all seeking to quantify and study relationships of some kind. The networks in network science model relationships as edges between nodes, which can represent anything from a species of flower, to an atom in a crystal, to an individual in a society. To quantify properties of relationships, edges can be directed and/or weighted. NetworkX provides Python classes and functions to create and manipulate such networks with ease. By now, you should have a sense of the types of problems network science and NetworkX can solve. The following chapters will cover various applications of network science as well as related features of NetworkX, with examples of how they can be applied to real datasets.
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You're reading from Network Science with Python and NetworkX Quick Start Guide
Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.
Read more about Edward L. Platt
Author (1)
Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.
Read more about Edward L. Platt