From 545b70f7efb2c4e36716167f5c44e17de1c96e2a Mon Sep 17 00:00:00 2001 From: lucashu1 Date: Tue, 15 May 2018 21:15:57 -0700 Subject: [PATCH] Update README: twitter --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eabeca6..9bec89c 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ Link Prediction Experiments **This repository contains a series of machine learning experiments for [link prediction](https://www.cs.cornell.edu/home/kleinber/link-pred.pdf) within social networks.** -We first implement and apply a variety of link prediction methods to each of the ego networks contained within the [SNAP Facebook dataset](https://snap.stanford.edu/data/egonets-Facebook.html) and to various [random networks](https://networkx.github.io/documentation/networkx-1.10/reference/generators.html) generated using [networkx](https://networkx.github.io/), and then calculate and compare the ROC AUC, Average Precision, and runtime of each method. +We first implement and apply a variety of link prediction methods to each of the ego networks contained within the [SNAP Facebook dataset](https://snap.stanford.edu/data/egonets-Facebook.html) and [SNAP Twitter dataset](https://snap.stanford.edu/data/egonets-Twitter.html), as well as to various [random networks](https://networkx.github.io/documentation/networkx-1.10/reference/generators.html) generated using [networkx](https://networkx.github.io/), and then calculate and compare the ROC AUC, Average Precision, and runtime of each method. ### Link Prediction Methods Tested: * [(Variational) Graph Auto-Encoders](https://arxiv.org/abs/1611.07308): An end-to-end trainable convolutional neural network model for unsupervised learning on graphs