This is the Windows app named node2vec whose latest release can be downloaded as node2vecsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named node2vec with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
node2vec
Ad
DESCRIPTION
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible.
Features
- Implementation of the node2vec algorithm for graph embeddings
- Biased random walk strategy to balance breadth-first and depth-first exploration
- Generates node embeddings for classification, clustering, and link prediction
- Reference implementation for the KDD 2016 paper on node2vec
- Scalable to large graphs through efficient sampling and optimization
- Provides reproducible experiments for graph-based machine learning tasks
Programming Language
Python, Scala
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/node2vec.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
