![]() If you don’t want to use the docker container, that’s fine too: you can follow the instructions at the bottom of the page for installing graspologic locally for more details. The password for the session is graphbook, or any password you choose to enter. The port is so that your browser can communicate with the jupyter session inside the docker container (which runs jupyter internally on port 8888), and so you are basically telling your computer that your port should “tie in” to port 8888 in the docker container. If you don’t know what port to choose, it’s probably easiest to try 8888, and if that doesn’t work, 8889, and keep working up until you find an open port. If it does not, open up a browser of your choice, and go to localhost:, where you will be prompted to enter the log-in password for your session. Which will launch a jupyter-lab session, and should automatically log you into the session in your browser. $ docker run -it -rm -p :8888 neurodata/graph-stats-book jupyter-lab -ip=0.0.0.0 -port=8888 /home/book/ -NotebookApp.token="graphbook" If you have a ubuntu/mac operating system and the docker daemon running on your computer, you can open up a terminal session and type the following command: Once you have docker installed on your computer, you can obtain the docker container for the book relatively easily. Obtaining the docker container for the textbook # We aren’t going to sit here and pretend to be the experts on docker, but fortunately the people over at docker won’t leave you on your own for that one! To install docker, check out their installation guide at. It might not be better than sliced bread, but it might be the next best thing! This means that you can, with a very small number of button clicks, create deployable software that thousands of people can use at the drop of a hat. While most of the packages required to run the contents of this book can be installed relatively easily via a combination of git, pip, and virtual environments, the easiest and fastest way to get you coding and interacting with real python code is docker.ĭocker is a containerization utility that, in effect, allows you to run standalone software in a separate area of your computer (called a docker container), that allows software to operate without conflicting with your local operating system. Getting software to run across multiple operating systems, particularly software with lots of dependencies, can range from difficult to impossible. To ease your ability to interact directly with the code of this book, we’ve developed a standalone docker container that you can use. Don’t be afraid to walk through these examples with your laptop in a jupyter notebook. RDPGs and more general network modelsįor this section, you will start to get your hands dirty with some real network datasets. Random walk and diffusion-based methodsġ0.1. Anomaly Detection For Timeseries of Networksĩ.1. Two-sample hypothesis testing in SBMsĨ.1. Testing for Differences between Groups of Edgesħ.1. Applications When You Have One NetworkĦ.2. Multiple-Network Representation LearningĦ. Estimating Parameters in Network Models via MLEĥ.4. Network Models with Network Covariatesĥ.1. Structured Independent Edge Model (SIEM)Ĥ.8. ![]() Degree-Corrected Stochastic Block Model (DCSBM)Ĥ.6. Inhomogeneous Erdos Renyi (IER) Random Network ModelĤ.5. Properties of Networks as a Statistical ObjectĤ.4. Discover and Visualize the Data to Gain Insightsģ. Prepare the Data for Network AlgorithmsĢ.6. End-to-end Biology Network Machine Learning ProjectĢ.3. ![]() Types of Network Machine Learning Problemsġ.4. The Network Machine Learning Landscapeġ.3. Hands-on Network Machine Learning with Scikit-Learn and Graspologicġ.
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