Replace link for GraphSAGE, the https://snap.stanford.edu/graphsage/ …

…is down, not sure if it is permanent or temporary

docs/source/models_and_datasets.md

@@ -23,6 +23,6 @@ Morpheus comes with a number of pre-trained models with corresponding training,
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 |Anomalous Behavior Profiling (ABP)|2015MiB|This model is an example of a binary classifier to differentiate between anomalous GPU behavior such as cryptocurrency mining / GPU malware, and non-anomalous GPU-based workflows (for example, ML/DL training). The model is an XGBoost model.|
 |Digital Fingerprinting (DFP)|4.97MiB|This use case is currently implemented to detect changes in a users' behavior that indicates a change from a human to a machine or a machine to a human. The model is an ensemble of an Autoencoder and fast Fourier transform reconstruction.|
-|Fraud Detection|76.55MiB|This model shows an application of a graph neural network for fraud detection in a credit card transaction graph. A transaction dataset that includes three types of nodes, transaction, client, and merchant nodes is used for modeling. A combination of [GraphSAGE](https://snap.stanford.edu/graphsage/) along with [XGBoost](https://xgboost.readthedocs.io/en/stable/) is used to identify frauds in the transaction networks.|
+|Fraud Detection|76.55MiB|This model shows an application of a graph neural network for fraud detection in a credit card transaction graph. A transaction dataset that includes three types of nodes, transaction, client, and merchant nodes is used for modeling. A combination of [GraphSAGE](https://github.com/williamleif/GraphSAGE) along with [XGBoost](https://xgboost.readthedocs.io/en/stable/) is used to identify frauds in the transaction networks.|
 |Ransomware Detection Model|n/a|This model shows an application of DOCA AppShield to use data from volatile memory to classify processes as ransomware or benign. This model uses a sliding window over time and feeds derived data into a random forest classifiers of various lengths depending on the amount of data collected.|
 |Flexible Log Parsing|1612MiB|This model is an example of using Named Entity Recognition (NER) for log parsing, specifically [Apache HTTP Server](https://httpd.apache.org/) logs.|