@@ -47,11 +47,8 @@ Use the built-in continuous integration in GitLab.
## Description
A scalable Framework for learning representation for network nodes, while preserving the neighborhood topology and node contextual attributes in the Learned Embedding. This method improves upon the random walk approach through a decision function, which learn quality vector representations useful for different downstream learning tasks. The probability walk builds a node corpus comprising sampled nodes with similar contextual attributes and structural patterns, which is trained using a NLP (natural language processing) technique. We evaluate the effectiveness of our model by comparing it with other baseline methods on three downstream tasks: Clustering, Link Prediction, and Node Classification. Our model shows better performance compared to these baselines.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
