Assignment on graph processing using GraphX in Apache Spark
Generate a un-directional graph RDD from a given graph data.
Compute listed vertex-based similarity measures for all the pairs of nodes in label data file. These similarity measures are computed between two nodes by utilizing neighborhood and/or node information of both nodes. Common neighbors Jaccard coefficient Adamic/Adar Preferential Attachment
Bonus Question: Link Prediction Model Using the measures generated from the graph and labels from the labeled data to predict the possibility of new link formation. Please use the following steps.
- Create a dataset by combining measures ( as features) and class labels from the labeled data.
- Use decision tree based algorithm in SparkML to train the prediction model.
- Split the dataset to generate the training data and testing data.
- Use training data to build model and testing data to evaluate the model.
- Present the model performance metrics: Accuracy, Recall, and Precision.
Instructions: Please follow the program submission instructions ( same as the previous assignments) Must use spark and GraphX for generating measures and use SparkML for bonus questions. More explanation on above graph measures here (https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-13-S3-S5)
Data Graph data Use this link (https://www.dropbox.com/s/ypcsynzo28fp8pt/graph_1965_1969.csv.zip? dl=0) to download graph data and file is formatted as shown below
click here (hhttps://www.dropbox.com/s/oehg91f7k9zy4bj/labeled_1965_1969_1970_1974.csv.zip ?dl=0) to download label data
Full solution code for eclipse with configurations: https://drive.google.com/open?id=1T6ttlJioo_LuqQG3jOY3X6QnfDbfma3Y
Code with image and results: http://adeolasiwoku.blogspot.com/2018/08/assignment-on-graph-processing-using.html