1. As the inventors of the property graph, Neo4j is the first and dominant mover in the graph market. A label is a named graph construct that is used to group nodes into sets. This Jupyter notebook is hosted here in the Neo4j Graph Data Science Client Github repository. list Procedure. Link prediction can involve both seen and unseen entities, hence patterns seen-to-unseen and unseen-to-unseen. Neo4j’s First Mover Advantage is Connecting Everyone to Graphs. Conductance metric. Execute either of these using the Python GDS client: pipe = gds. 27 Load your in- memory graph with labels & features Use linkPrediction. Any help on this would be appreciated! Attached screenshots. Early control of the related risk factors is crucial to reduce the incidence of DME. Reload to refresh your session. 4M views 2 years ago. The Neo4j GDS Machine Learning pipelines are a convenient way to execute complex machine learning workflows directly in the Neo4j infrastructure. 1. beta. Since the model has been trained on features which are created using the feature pipeline, the same feature pipeline is stored within the model and executed at prediction time. Topological link prediction. Link Prediction with Neo4j Part 1: An Introduction This is the beginning of a series of posts about link prediction with Neo4j. The question mark denotes an edge to predict. i. e. So just to confirm the training metrics I receive are based on predicting all types of relationships between the 2 labels I have provided right? So in my case since all the provided links are between A-B those will be the positive samples and as far as negative sample. Assume we need to calculate Link Prediction chances between node U & node V in the below scenarios Hands-On Graph Analytics with Neo4j (oreilly. You can add an existing node property to the link prediction pipeline by adding it to your graph projection -> CALL gds. Between these 50,000 nodes are 2. For each algorithm in the Algorithms pages we have small examples of limited scope that demonstrate the usage of that particular algorithm, typically only using that one algorithm. Several similarity metrics can be used to compute a similarity score. PyKEEN is a Python library that features knowledge graph embedding models and simplifies multi-class link prediction task executions. You need no prior knowledge of other NoSQL databases, although it is helpful to have read the guide on graph databases and understand basic data modeling questions and concepts. The algorithm trains a single-layer feedforward neural network, which is used to predict the likelihood that a node will occur in a walk based on the occurrence of another node. The relationship types are usually binary-labeled with 0 and 1; 0. 0. The input graph contains default node values or node values from a graph projection. - 57884This Week in Neo4j: New GraphAcademy Course, Road to NODES Workshops, Link Prediction Pipelines, Graph Native Storage, and More FEATURED NODES SPEAKER: Dagmar Waltemath Using the examples of COVID. Update the cell below to use the Bolt URL, and Password, as you did previously. The triangle count of a node is useful as a features for classifying a given website as spam, or non-spam. linkPrediction. For more information on feature tiers, see API Tiers. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. One of the primary features added in the last year are support for heterogenous graphs and link neighbor loaders. Neo4j图分析—链接预测算法（Link Prediction Algorithms） 链接预测是图数据挖掘中的一个重要问题。链接预测旨在预测图中丢失的边, 或者未来可能会出现的边。这些算法主要用于判断相邻的两个节点之间的亲密程度。通常亲密度越大的节点之间的亲密分值越. Because cloud images are based on the standard Neo4j Debian package, file locations match the file locations described in the Neo4j. Running a lunch and learn session with colleagues. NEuler: The Graph Data. beta. beta . The hub score estimates the value of its relationships to other nodes. 2. Lastly, you will store the predictions back to Neo4j and evaluate the results. . By mapping GraphQL type definitions to the property graph model used by Neo4j, the Neo4j GraphQL Library can generate a CRUD API backed by Neo4j. Neo4j Link prediction ML Pipeline Ask Question Asked 1 year, 3 months ago Modified 1 year, 2 months ago Viewed 216 times 1 I am working on a use case predict. Name your container (avoids generic id) docker run --name myneo4j neo4j. These methods compute a score for a pair of nodes, where the score could be considered a measure of proximity or “similarity” between those nodes based on the graph topology. Working code and sample data sets from both Spark and Neo4j are included to ensure concepts. Let us take a look at a few options available with the docker run command. com Adding link features. The algorithm calculates shortest paths between all pairs of nodes in a graph. Total Neighbors is computed using the following formula: where N (x) is the set of nodes adjacent to x, and N (y) is the set of nodes adjacent to y. This network has 50,000 nodes of 11 types — which we would call labels in Neo4j. In a graph, links are the connections between concepts: knowing a friend, buying an item, defrauding a victim, or even treating a disease. Such an example is the method proposed in , which builds a heterogeneous network and performs link prediction to construct an integrative model of drug efficacy. The graph we will be working with is the MovieLens dataset, which is handily available as a Neo4j Sandbox project. *` it does predictions of new possible neighbors for all nodes in the graph. The algorithm trains a single-layer feedforward neural network, which is used to predict the likelihood that a node will occur in a walk based on the occurrence of another node. These are your slides to personalise, update, add to and use to help you tell your graph story. It is often used to find nodes that serve as a bridge from one part of a graph to another. We can then use the link prediction model to, for instance, recommend the. Reload to refresh your session. To help you along your path of learning more about Neo4j, we want to provide you with the resources we used throughout this section, as well as a few additional resources for. Real world, log-, sensor-, transaction- and event data is noisy. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. Hi, I was wondering if it would be at all possible to access the test predictions during the training phase of the link prediction pipeline to better understand the types of predictions the model is getting right and wrong. Link Prediction Pipelines. History and explanation. train, is responsible for splitting data, feature extraction, model selection, training and storing a model for future use. Each algorithm requiring a trained model provides the formulation and means to compute this model. We will need to execute the docker run command with the neo4j image and specify any options or versions we want along with that. The exam tests your knowledge of developer-focused concepts, including the graph model, Cypher, and more. Back-up graphs and models to disk. This repository contains a series of machine learning experiments for link prediction within social networks. Sweden +46 171 480 113. Neo4j sharding contains all of the fabric graphs (instances or databases) that are managed by a coordinating fabric database. Link Prediction Pipeline not working with GraphSage · Issue #214 · neo4j/graph-data-science · GitHub. Running this. The name of a pipeline. Link prediction explores the problem of predicting new relationships in a graph based on the topology that already exists. Add this topic to your repo. com) In the left scenario, X has degree 3 while on. It has the following use cases: Finding directions between physical locations. The Neo4j GDS library includes the following community detection algorithms, grouped by quality tier: Production-quality. This is also true for graph data. You should have created an Neo4j AuraDB. . The following algorithms use only the topology of the graph to make predictions about relationships between nodes. This trains a model by minimizing a loss function which depends on a weight matrix and on the training data. To install Python libraries in (2) you can use pip!pip install neo4j-driver!pip install graphdatascience Connect to Neo4j. The loss can be minimized for example using gradient descent. 5, and the build-in machine learning models, has now given the Data Scientist that needs to perform a machine learning task on any graph in Neo4j two possible routes to a solution. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. Make graph-specific predictions such as link prediction; Explore the latest version of Neo4j to build a graph data science pipeline;ETL Tool Steps and Process. 1. In this guide we’re going to use these techniques to predict future co-authorships using AWS SageMaker Autopilot and link prediction algorithms from the Graph Data Science Library. Link Prediction using Neo4j and Python. We will look into which steps are required to create a link prediction pipeline in a homogenous graph. In a graph, links are the connections between concepts: knowing a friend, buying an item, defrauding a victim, or even treating a disease. mutate", but the python client somehow changes the input function name to lowercase characters. Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learning Purchase of the print or Kindle book includes a free PDF eBook. Users are therefore encouraged to increase that limit to a realistic value of 40000 or more, depending on usage patterns. Suppose you want to this tool it to import order data into Neo4j. graph. x exposed as Cypher procedures. graph. Beginner. However, in real-world scenarios, type. I am new to AI and ML and interested in application of ML in graph database especially in finance sector. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. The Resource Allocation algorithm was introduced in 2009 by Tao Zhou, Linyuan Lü, and Yi-Cheng Zhang as part of a study to predict links in various networks. When I install this library using the procedure mentioned in the following link my database stops working and I have to delete it. Readers will understand how and when to apply graph algorithms – including PageRank, Label Propagation and Louvain Modularity – in addition to learning how to create a machine learning workflow for link prediction that combines Neo4j and Spark. Apply the targetNodeLabels filter to the graph. Understanding Neo4j GDS Link Predictions (with Demonstration) Let’s explore how Neo4j GDS Link…There are 2 ways of prediction: Exhaustive search, Approximate search. History and explanation. 9 - Building an ML Pipeline in Neo4j Link Prediction Deep Dive - YouTube Exploring Supervised Entity Resolution in Neo4j - Neo4j Graph Database Platform. pipeline. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. The Neo4j Graph Data Science library offers the feature of machine learning pipelines to design an end-to-end workflow, from graph feature extraction to model training. Alpha. This means that a lot of our relationships will point back to. 2. config. Each decision tree is typically trained on. Here are the CSV files. The neo4j-admin import tool allows you to import CSV data to an empty database by specifying node files and relationship files. semi-supervised and representation learning. This means developers don’t even need to implement GraphQL. Table 1. There are two ways of running the Neo4j Graph Data Science library in a composite deployment, both of which are covered in this section: 1. Star 458. Logistic regression is a fundamental supervised machine learning classification method. Regards, CobraSure, below is some sample code where I have a created a link prediction pipeline and am trying to predict links between two labels (A and B). The objective of this page is to give a brief overview of the methods, as well as advice on how to tune their. We’re going to use this tool to import ontologies into Neo4j. Neo4j’s recommended value for negativeSamplingRatio is the true class ratio of the graph . History and explanation. You signed in with another tab or window. Neo4j (version 4. Here’s how to train and optimize Link Prediction models in Neo4j Graph Data Science to get the best results. At the moment, the pipeline features three different. The Neo4j GDS library includes the following pipelines to train and apply machine learning models, grouped by quality tier: Beta. Heap size. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. UK: +44 20 3868 3223. Divide the positive examples and negative examples into a training set and a test set. Using labels as filtering mechanism, you can render a node’s properties as a JSON document and insert. GRAPH ANALYTICS: Relationship (Link) Prediction in Graphs Using Neo4j. In a graph, links are the connections between concepts: knowing a friend, buying an item, defrauding a victim, or even treating a disease. GDS Configuration Settings. Property graph model concepts. Using Hadoop to efficiently pre-process, filter and aggregate raw information to be suitable for Neo4j imports is a reasonable approach. Online and classroom training - using these published guides in the classroom allows attendees to work through the material at their own pace and have access to the guide 24/7 after class ends. The Louvain method is an algorithm to detect communities in large networks. The compute function is executed in multiple iterations. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. For link prediction, it must be a list of length 2 where the first weight is for negative examples (missing relationships) and the second for positive examples (actual relationships). However, in this post,. For the latest guidance, please visit the Getting Started Manual . Link prediction is all about filling in the blanks – or predicting what’s going to happen next. We are dealing with a binary classification problem, where we want to predict if a link exists between a pair of. US: 1-855-636-4532. gds. AmpliGraph: Link prediction with ComplEx. The closer two nodes are, the more likely there. A graph in GDS is an in-memory structure containing nodes connected by relationships. Since FastRP is a random algorithm and inductive only for propertyRatio=1. You can learn more and buy the full video course here [everyone, I am Ayush Baranwal, a new joiner to neo4j community. This website uses cookies. 1. Yes correct. node2Vec . Thus, in evaluating link prediction methods, we will generally use two parameters training and test (each set to 3 below), and de ne the set Core to be all nodes incident to at least training edges in G[t0;t0 0] and at least test edges in G[t1;t0 1]. How can I get access to them?Link prediction algorithms help determine the closeness of a pair of nodes using the topology of the graph. We can now use the SVM model to predict links in our Neo4j database since it has been trained and validated. The GDS library runs within a Neo4j instance and is therefore subject to the general Neo4j memory configuration. Navigating Neo4j Browser. In addition to the predicted class for each node, the predicted probability for each class may also be retained on the nodes. Lastly, you will store the predictions back to Neo4j and evaluate the results. In order to be able to leverage topological information about. For these orders my intention is to predict to whom the order was likely intended to. x and Neo4j 4. mutate( graphName: String, configuration: Map ) YIELD preProcessingMillis: Integer, computeMillis: Integer, postProcessingMillis: Integer, mutateMillis: Integer, relationshipsWritten: Integer, probabilityDistribution: Integer, samplingStats: Map. The goal of pre-processing is to provide good features for the learning algorithm. Centrality. . Topological link prediction. Sample a number of non-existent edges (i. Just like in the GDS procedure API they do not take a graph as an argument, but rather two node references as positional arguments. Since the model has been trained on features which are created using the feature pipeline, the same feature pipeline is stored within the model and executed at prediction time. Get started with GDSL. We’ll start the series with an overview of the problem and…Triangle counting is a community detection graph algorithm that is used to determine the number of triangles passing through each node in the graph. So I would like to be able to see the set of nodes, test prediction, and actual label (0 or 1). In the logs I can see some of the. The GDS implementation of HashGNN is based on the paper "Hashing-Accelerated Graph Neural Networks for Link Prediction", and further introduces a few improvements and generalizations. The Neo4j Graph Data Science library support the following node property prediction pipelines: Beta. Divide the positive examples and negative examples into a training set and a test set. We’re going to learn how to use the link prediction algorithms with the help of a small friends graph. This seems because you want to predict prospective edges in a timeserie. Where the options for <replan-type> are: force (to recompile the query, whether it is in the cache or not) skip (recompile only if the query is not in the cache) In general, if you want to force a replan, then you would do something like this: CYPHER replan=force EXPLAIN <query>. Enhance and accelerate data predictions with Neo4j Graph Data Science. Follow the Neo4j graph database blog to stay up to date with all of the latest from the world's leading graph database. We also learnt about the challenge of splitting train and test data sets when working with graphs. Often the graph used for constructing the embeddings and. jar. Importing the Data in-memory graph International Airport ipykernel iterations jpy-console jupyter Label Propagation libraries link prediction Louvain machine learning MATCH matplotlib Minimum Spanning Tree modularity nodes number of relationships. The following algorithms use only the topology of the graph to make predictions about relationships between nodes. Node2Vec is a node embedding algorithm that computes a vector representation of a node based on random walks in the graph. I referred to the co-author link prediction tutorial, in that they considered all pair of nodes that don’t. In fact, of all school subjects, it’s the most consistently derided in pop culture (which is the. Submit Search. Neo4j Browser built-in guides. Now that the application is all set up, there are only a few steps to import data. 1. 7 can replicate similar G-DL models out there. Topological link predictionNeo4j Live: Building a Recommendation Engine with Neo4j GDS - An Introduction to Link Prediction In this Neo4j Live event I explain how the Neo4j GDS can be utilized to build a recommendation engine. Preferential attachment means that the more connected a node is, the more likely it is to receive new links. Node Classification Pipelines. 1. Link prediction pipelines. What I want is to add existing node property from my projected graph to the pipeline - 57884I did an estimate before training, and the mem available is less than required. 1. Then open mongo-shell and run:Neo4j Sandbox - each sandbox comes with a built-in, default guide to help you get started with whichever sandbox you chose!. Things like node classifications, edge predictions, community detection and more can all be performed inside. Topological link prediction - these algorithms determine the closeness of. Hello Do you have a name property on your source and target node? Regards, Cobra - 57884Then, if you follow this example , it should help you solve your use case. The first one predicts for all unconnected nodes and the second one applies KNN to predict. pipeline. In addition to the predicted class for each node, the predicted probability for each class may also be retained on the nodes. Read More. The feature vectors can be obtained by node embedding techniques. Column to Node Property - columns (fields) on the relational tables. node pairs with no edges between them) as negative examples. For link prediction, it must be a list of length 2 where the first weight is for negative examples (missing relationships) and the second for positive examples (actual relationships). com) In the left scenario, X has degree 3 while on. The computed scores can then be used to. Gather insights and generate recommendations with simple cypher queries, by navigating the graph. Users can write patterns similar to natural language questions to retrieve data and traverse layers of the graph. A feature step computes a vector of features for given node pairs. In most machine learning scenarios, several pre-processing steps are applied to produce data that is amenable to machine learning algorithms. The following algorithms use only the topology of the graph to make predictions about relationships between nodes. Link prediction algorithms help determine the closeness of a pair of nodes using the topology of the graph. The problem is treated as a supervised link prediction problem on a homogeneous citation network with nodes representing papers (with attributes such as binary keyword indicators and categorical. The train mode, gds. create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph or incoming graph data. France: +33 (0) 1 88 46 13 20. Link Prediction algorithms or rather functions help determine the closeness of a pair of nodes. It is computed using the following formula: where N (u) is the set of nodes adjacent to u. Things like node classifications, edge predictions, community detection and more can all be. For more information on feature tiers, see. He uses the publicly available Citation Network dataset to implement a prediction use case. neosemantics (n10s) neosemantics is a plugin that enables the use of RDF and its associated vocabularies like OWL, RDFS, SKOS, and others in Neo4j. Preferential Attachment is a measure used to compute the closeness of nodes, based on their shared neighbors. I am not able to get link prediction algorithms in my graph algorithm library. You should have a basic understanding of the property graph model . The neighborhood is sampled through random walks. ”. mutate Train a Link Prediction Model in Neo4j Link Prediction: Predicting unobserved edges or relationships that will form in the future Neo4j Automates the Tricky Parts: 1. Graphs are everywhere. For each node. It is computed using the following formula:In this blog post, I will present how you can fetch data from Neo4j to create movie recommendations in PyTorch Geometric. Most of the data frames don’t add new information but are repetetive. Introduction. This chapter is divided into the following sections: Syntax overview. You signed out in another tab or window. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. The generalizations include support for embedding heterogeneous graphs; relationships of different types are associated with different hash functions, which. Graph management. FastRP and kNN example. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. commonNeighbors(node1:Node, node2:Node, { relationshipQuery: "rel1", direction: "BOTH" }) So are you. Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo4j at Pharma Data UK 2022 - Download as a PDF or view online for free. FastRP and kNN example Defaults and Limits. PyG released version 2. Betweenness Centrality. 1) I want to the train set to have only positive samples i. Neo4j Graph Data Science. You signed in with another tab or window. Neo4j Graph Data Science is a library that provides efficiently implemented, parallel versions of common graph algorithms for Neo4j 3. Restore persisted graphs and models to memory. e. All nodes labeled with the same label belongs to the same set. Eigenvector Centrality. which has provided promising results in accuracy, even more so in the computational efficiency, similar to our results in DTP. The fabric database is actually a virtual database that cannot store data, but acts as the entrypoint into the rest of the graphs. If authentication is enabled for Neo4j, set the NEO4J_AUTH environment variable, containing username and password: export NEO4J_AUTH=user:password. Link prediction analysis from the book ported to GDS Neo4j Graph Data Science and Graph Algorithms plugins are not compatible, so they do not and will not work together on a single instance of Neo4j. 1. A value of 0 indicates that two nodes are not in the same community. Allow GDS in the neo4j. Reload to refresh your session. Under the hood, the link prediction model in Neo4j uses a logistic regression classifier. Link prediction explores the problem of predicting new relationships in a graph based on the topology that already exists. Split the input graph into two parts: the train graph and the test graph. linkPrediction. , graph not containing the relation between order & relation. We will understand all steps required in such a pipeline and cover common pit. Neo4j link prediction (or link prediction for any graph database) is the problem of predicting the likelihood of a connection or a relationship between two nodes. As with many of the centrality algorithms, it originates from the field of social network analysis. This has been an area of research for. CELF. In this blog post, I will present how you can fetch data from Neo4j to create movie recommendations in PyTorch Geometric. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. Concretely, Node Regression models are used to predict the value of node property. You can follow the guides below. I use the run_cypher function, and it works. The Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes based on two scores, a hub score and an authority score. Ensure that MongoDB is running a replica set. Neo4j is the leading graph database platform that drives innovation and competitive advantage at Airbus, Comcast, eBay, NASA, UBS, Walmart and more. The Neo4j GDS library includes the following centrality algorithms, grouped by quality tier: Production-quality. Link Prediction with Neo4j In this week’s Neo4j Online Meetup , Amy Hodler and I presented Link Prediction with Neo4j. Although Neo4j has traditionally been used for transaction workloads, in recent years it is increasingly being used at the heart of graph analytics platforms. By clicking Accept, you consent to the use of cookies. I would suggest you use a single in-memory subgraph that contains both users and restaurants. You will learn how to take data from the relational system and to. Thank you Ayush BaranwalThe train mode, gds. It is the easiest graph language to learn by far because of. I do not want both; rather I want the model to predict the. Random forest is a popular supervised machine learning method for classification and regression that consists of using several decision trees, and combining the trees' predictions into an overall prediction. Main Memory. So, I was able to train the model and the model is now ready for predictions. 9. A value of 1 indicates that two nodes are in the same community. There are many metrics that can be used in a link prediction problem. Below is a list of guides with descriptions for what is provided. alpha. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. linkPrediction. Graph Databases as Part of an AWS Architecture1. If not specified, all pipelines in the catalog are listed. Link Prediction problems tend to be highly imbalanced with way more negative examples possible in the graph than positive ones — it is an O(n²) problem. The other algorithm execution modes - stats, stream and write - are also supported via analogous calls. By clicking Accept, you consent to the use of cookies. . On a high level, the link prediction pipeline follows the following steps: Link Prediction techniques are used to predict future or missing links in graphs. By following the meaningful relationships between the people and movies, you can determine occurences of actors working. The idea of link prediction algorithms is to be able to create a matrix N×N, where N is the number. The Neo4j Graph Data Science library offers the feature of machine learning pipelines to design an end-to-end workflow, from graph feature extraction to model training. Native graph databases like Neo4j focus on relationships. As part of our pipelines we offer adding such pre-procesing steps as node property. Divide the positive examples and negative examples into a training set and a test set. A value of 0 indicates that two nodes are not close, while higher values indicate nodes are closer. The Link Prediction pipeline in the Neo4j GDS library supports the following metrics: AUCPR OUT_OF_BAG_ERROR (only for RandomForest and only gives a validation score) The AUCPR metric is an abbreviation. Notice that some of the include headers and some will have separate header files. Neo4j link prediction (or link prediction for any graph database) is the problem of predicting the likelihood of a connection or a relationship between two nodes in a network. The Neo4j Graph Data Science library contains the following node embedding algorithms: 1. Link Prediction algorithms or rather functions help determine the closeness of a pair of nodes. Hi, How can I get link prediction between nodes of two in-memory graph: Description: Given a graph database contains: User, Restaurant and - 11527 This website uses cookies. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. It may be useful to generate node embeddings with GraphSAGE as a node property step in a machine learning pipeline (like Link prediction pipelines and Node property prediction). A model is generally a mathematical formula representing real-world or fictitious entities. This page is no longer being maintained and its content may be out of date. Node values can be updated within the compute function and represent the algorithm result. They can be developed by anyone - community members, partners, enterprises, and more - and are a convenient way of trying out ideas or building useful tools with Neo4j databases. Creating a pipeline. Link prediction algorithms help determine the closeness of a pair of nodes using the topology of the graph. Topological link prediction. Much of the graph is incomplete because the intial data is entered manually and often the person will create something link Child <- Mother, Child. The computed scores can then be used to predict new relationships between them. Node Classification PipelineThis section features guides and tutorials to help you understand how to deploy, maintain, and optimize Neo4j. The neural network is trained to predict the likelihood that a node. Cypher is Neo4j’s graph query language that lets you retrieve data from the graph. To facilitate machine learning and save time for extracting data from the graph database, we developed and optimized Decision Tree Plug-in (DTP) containing 24. A Link Prediction pipeline executes a sequence of steps to compute the features used by a machine learning model.