Creating link prediction metrics with Neo4j. 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. Video Transcript: Link Prediction With Python (Protein-Protein Interaction Example) Today we’re going to be going through a step-by-step demonstration of how to perform link prediction with Python in Neo4j’s Graph Data Science Library. 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. Neo4j Graph Data Science uses the Adam optimizer which is a gradient descent type algorithm. website uses cookies. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. I have a heterogenous graph and need to use a pipeline. 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. A value of 1 indicates that two nodes are in the same community. 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. Then, create another Heroku app for the front-end. 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. linkPrediction. In this guide, we will predict co-authorships using the link prediction machine learning model that was introduced in. Reload to refresh your session. , graph containing the relation between order & relation. The computed scores can then be used to predict new relationships between them. The following algorithms use only the topology of the graph to make predictions about relationships between nodes. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. 5. Oh ok, no worries. Often the graph used for constructing the embeddings and. 5. Semi-inductive: a larger, updated graph that includes and extends the training one. config. One such approach to perform link prediction on scholarly data, in Neo4j, has been performed by Sobhgol et al. Thank you Ayush BaranwalThe train mode, gds. You signed out in another tab or window. 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. --name. Each relationship starts from a node in the first node set and ends at a node in the second node set. Beginner. Having multiple in-memory graphs that don't encompass both restaurants and users is tricky, because you need the same feature size for restaurant and user nodes to be. This has been an area of research f. Was this page helpful? US: 1-855-636-4532. Sure, 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). 4M views 2 years ago. Prerequisites. Since the post, I took more time to dig deeper and learn the inner workings of the pipeline. Node Classification Pipelines. GraphSAGE and GCN are learned in an. In a graph, links are the connections between concepts: knowing a friend, buying an item, defrauding a victim, or even treating a disease. 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. Link prediction algorithms help determine the closeness of a pair of nodes using the topology of the graph. A graph in GDS is an in-memory structure containing nodes connected by relationships. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. The graph we will be working with is the MovieLens dataset, which is handily available as a Neo4j Sandbox project. There are many metrics that can be used in a link prediction problem. 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!. pipeline. 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. As part of our pipelines we offer adding such pre-procesing steps as node property. Each algorithm requiring a trained model provides the formulation and means to compute this model. 1. 1. There’s a common one-liner, “I hate math…but I love counting money. It measures the average farness (inverse distance) from a node to all other nodes. The Node Similarity algorithm compares each node that has outgoing relationships with each other such node. which has provided. Restore persisted graphs and models to memory. The heap space is used for storing graph projections in the graph catalog, and algorithm state. Generalization across graphs. Reload to refresh your session. Betweenness Centrality. Link prediction algorithms help determine the closeness of a pair of nodes using the topology of the graph. graph. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. A* is an informed search algorithm as it uses a heuristic function to guide the graph traversal. The graph filter on each step consists of contextNodeLabels + targetNodeLabels and contextRelationships + relationshipTypes. Beginner. Hi, I ran Neo4j's link prediction pipeline on a graph and would like to inspect and visualize the results through Cypher queries and graph viz. node2Vec . Node Classification PipelineThis section features guides and tutorials to help you understand how to deploy, maintain, and optimize Neo4j. Then an evaluation is performed on removed edges. Node Regression Pipelines. APOC Documentation Other Neo4j Resources Neo4j Graph Data Science Documentation Neo4j Cypher Manual Neo4j Driver Manual Cypher Style Guide Arrows App • APOC is a great plugin to level up your cypher • This documentation outlines different commands one could use • Link to APOC documentation • The Cypher manual can be. beta. create . The underlying assumption roughly speaking is that a page is only as important as the pages that link to it. Upload. nodeClassification. train, is responsible for data splitting, feature extraction, model selection, training and storing a model for future use. Running a lunch and learn session with colleagues. The team decided to create a knowledge graph stored in Neo4j, and devised a processing pipeline for ingesting the latest medical research. With a native graph database at the core, Neo4j offers Neo4j Graph Data Science — a library of graph algorithms for analysts and data scientists. If you are a Go developer, this guide provides an overview of options for connecting to Neo4j. I'm trying to construct a pipeline for link prediction to find novel links between the entity nodes. The computed scores can then be used to. Link Prediction on Latent Heterogeneous Graphs. 0 with contributions from over 60 contributors. 1. Ensure that MongoDB is running a replica set. You can manage as many projects and database servers locally as you like and also connect to remote Neo4j servers. drop (pipelineName: String, failIfMissing: Boolean) YIELD pipelineName: String, pipelineType: String, creationTime: DateTime, pipelineInfo: Map. nodeRegression. Usage in node classification Link prediction is all about filling in the blanks – or predicting what’s going to happen next. There are tools that support these types of charts for metrics and dashboarding. node pairs with no edges between them) as negative examples. In Python, “neo4j-driver” and “graphdatascience” libraries should be installed. Divide the positive examples and negative examples into a training set and a test set. Link Prediction; Connected Feature Extraction; Courses. Yeah, according to the documentation: relationshipTypes means: Filter the named graph using the given relationship types. UK: +44 20 3868 3223. The loss can be minimized for example using gradient descent. As you can see in both the training and prediction steps I specify that I am only interested in labels A and B and relationships between them ('rel1_labelA-l. You should be familiar with the orchestration framework on which you want to deploy. You will learn how to take data from the relational system and to. You signed in with another tab or window. The computed scores can then be used to predict new relationships between them. The neural network is trained to predict the likelihood that a node. The methods for doing Topological link prediction are a bit different. It is often used to find nodes that serve as a bridge from one part of a graph to another. During graph projection, new transactions are used that do not inherit the transaction state of. I know link prediction algorithms can predict between two nodes but I don't know for machine learning pipeline. Reload to refresh your session. This feature is in the alpha tier. pipeline. Topological link prediction. How do I turn this into a graph? My ultimate goal is to find relationships between entities or words with each other from. semi-supervised and representation learning. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. Running this mode results in a regression model of type NodeRegression, which is then stored in the model catalog . Enhance and accelerate data predictions with Neo4j Graph Data Science. This stores a trainable pipeline object in the pipeline catalog of type Node regression training pipeline . Link Prediction with Neo4j Part 1: An Introduction I’ve started a series of posts about link prediction and the algorithms that we recently added to the Neo4j Graph Algorithms library. addMLP Procedure. Users can write patterns similar to natural language questions to retrieve data and traverse layers of the graph. Shortest path is considered to be one of the classical graph problems and has been researched as far back as the 19th century. This page is no longer being maintained and its content may be out of date. In addition to the predicted class for each node, the predicted probability for each class may also be retained on the nodes. A label is a named graph construct that is used to group nodes into sets. Neo4j is designed to be very visual in nature. Run Link Prediction in mutate mode on a named graph: CALL gds. I am not able to get link prediction algorithms in my graph algorithm library. Similarity algorithms compute the similarity of pairs of nodes based on their neighborhoods or their properties. 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. PyG released version 2. Neo4j 4. streamRelationshipProperty( 'mygraph', 'predictied_probablity_score', ['predicted_relationship_name. Philipp Brunenberg explores the Neo4j Graph Data Science Link Prediction pipeline. GDS Feature Toggles. 0 with contributions from over 60 contributors. beta . By following the meaningful relationships between the people and movies, you can determine occurences of actors working. You should be familiar with graph database concepts and the property graph model . train, is responsible for splitting data, feature extraction, model selection, training and storing a model for future use. Setting this value via the ulimit. A Graph app is a Single Page Application (SPA) built with HTML and JavaScript which interact with Neo4j databases through Neo4j Desktop . g. Node classification pipelines. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. . Node Classification Pipelines, Node Regression Pipelines, and Link Prediction Pipelines are trained using supervised machine learning methods. Builds logistic regression models using. The model catalog is a concept within the GDS library that allows storing and managing multiple trained models by name. France: +33 (0) 1 88 46 13 20. PyKEEN is a Python library that features knowledge graph embedding models and simplifies multi-class link prediction task executions. Preferential attachment means that the more connected a node is, the more likely it is to receive new links. We have a lot of things we want to do for upcoming releases so cannot promise we'll get to this in the near future however. I am not able to get link prediction algorithms in my graph algorithm library. Sweden +46 171 480 113. This is also true for graph data. PyG released version 2. France: +33 (0) 1 88 46 13 20. 1. 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. 1. Not knowing before, there is an example in pyG that also uses the MovieLens dataset for a link. We want to use the K-Nearest Neighbors algorithm (kNN) to identify similar customers and base our product recommendations on that. This chapter is divided into the following sections: Syntax overview. This guide explains how to run Neo4j on orchestration frameworks such as Mesosphere DC/OS and Kubernetes. I would suggest you use a single in-memory subgraph that contains both users and restaurants. The feature vectors can be obtained by node embedding techniques. . To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. Okay. Conductance metric. The first one predicts for all unconnected nodes and the second one applies KNN to predict. gds. Using labels as filtering mechanism, you can render a node’s properties as a JSON document and insert. Lastly, you will store the predictions back to Neo4j and evaluate the results. " GitHub is where people build software. Not knowing before, there is an example in pyG that also uses the MovieLens dataset for a link prediction. Sample a number of non-existent edges (i. In a graph, links are the connections between concepts: knowing a friend, buying an item, defrauding a victim, or even treating a disease. This has been an area of research for many years, and in the last month we've introduced link prediction algorithms to the Neo4j Graph Algorithms library. This represents a configurable pipeline that can later be invoked for training, which in turn creates a. It depends on how it will be prioritized internally. Neo4j Bloom is a data exploration tool that visualizes data in the graph and allows users to navigate and query the data without any query language or programming. The task we cover here is a typical use case in graph machine learning: the classification of nodes given a graph and some node. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. Videos, text, examples, and code are just some of the formats in which we deliver the information to encourage you and aid all learning styles. A Link Prediction pipeline executes a sequence of steps to compute the features used by a machine learning model. 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. History and explanation. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. GDS Configuration Settings. backup Procedure. The definition from Neo4j’s developer manual in the paragraph below best explains what labels do and how they are used in the graph data model. The computed scores can then be used to predict new relationships between them. Node embeddings are typically used as input to downstream machine learning tasks such as node classification, link prediction and kNN similarity graph construction. Here are the CSV files. gds. One of the primary features added in the last year are support for heterogenous graphs and link neighbor loaders. The algorithms are divided into categories which represent different problem classes. You will then use the Neo4j Python driver to fetch the data and transform it into a PyKE EN graph. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. Thanks for your question! There are many ways you could approach creating your relationships. On Heroku > Settings > Config Vars, add the credentials to connect to the database hosted Neo4j AuraDB (or the sandbox if you haven’t migrated to AuraDB). I would suggest you use a single in-memory subgraph that contains both users and restaura. run_cypher("""CALL gds. Notice that some of the include headers and some will have separate header files. Pipeline. PyKEEN is a Python library that features knowledge graph embedding models and simplifies multi-class link prediction task executions. Fork 122. For predicting the link between the nodes, we are going to need the following tools and libraries: Neo4j Database;Node Classification Pipelines, Node Regression Pipelines, and Link Prediction Pipelines are trained using supervised machine learning methods. Link prediction algorithms help determine the closeness of a pair of nodes using the topology of the graph. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. Neo4j’s First Mover Advantage is Connecting Everyone to Graphs. Apparently, the called function should be "gds. How can I get access to them?The neo4j-admin import tool allows you to import CSV data to an empty database by specifying node files and relationship files. 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. Back-up graphs and models to disk. List of all alpha machine learning pipelines operations in the GDS library. graph. Pytorch Geometric Link Predictions. Neo4j Graph Data Science. The objective of this page is to give a brief overview of the methods, as well as advice on how to tune their. Tried gds. 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. Concretely, Node Regression models are used to predict the value of node property. Row to Node - each row in a relational entity table becomes a node in the graph. The regression model can be applied on a graph in the graph catalog to predict a property value for previously unseen nodes. I am not able to get link prediction algorithms in my graph algorithm library. 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 for the Area Under the Precision-Recall Curve metric. You switched accounts on another tab or window. The neighborhood is sampled through random walks. But thanks for adding it as future candidate and look forward to utilizing it once it comes out - 58793Neo4j is a graph database that includes plugins to run complex graph algorithms. “A deep dive into Neo4j link prediction pipeline and FastRP embedding algorithm” Optuna documentation; Special thanks to Jacob Sznajdman and Tomaz Bratanic who helped with the content and review of this blog post! Also, a special thanks to Alessandro Negro for his valuable insights and coding support for this post!After training, the runnable model is of type NodeClassification and resides in the model catalog. This tutorial formulates the link prediction problem as a binary classification problem as follows: Treat the edges in the graph as positive examples. Meetups and presentations - presenters. You switched accounts on another tab or window. Configure a default. Never miss an update by subscribing to the weekly Neo4j blog newsletter. This means that a lot of our relationships will point back to. Link prediction algorithms help determine the closeness of a pair of nodes using the topology of the graph. Introduction. x exposed as Cypher procedures. This is done with the following snippetyes, working now. Graphs are stored using compressed data structures optimized for topology and property lookup operations. Hi , The link prediction API as it currently stands is not really designed for real-time inferences. The first step of building a new pipeline is to create one using gds. Let us take a look at a few options available with the docker run command. Pregel is a vertex-centric computation model to define your own algorithms via a user-defined compute function. Things like node classifications, edge predictions, community detection and more can all be. This network has 50,000 nodes of 11 types — which we would call labels in Neo4j. commonNeighbors(node1:Node, node2:Node, { relationshipQuery: "rel1", direction: "BOTH" }) So are you. Building an ML Pipeline in Neo4j: Link Prediction Deep DiveHands on deep dive into building a link prediction model in Neo4j, not just covering the marketing. addNodeProperty - 57884HI Mark, I have been following your excellent two articles and applying the learning to my (anonymised) graph of connections between social care clients. 1. Topological link prediction. Neo4j is a graph database that includes plugins to run complex graph algorithms. By clicking Accept, you consent to the use of cookies. which has provided promising results in accuracy, even more so in the computational efficiency, similar to our results in DTP. Parameters. The library includes algorithms for community detection, centrality, node similarity, pathfinding, and link prediction. This algorithm was popularised by Albert-László Barabási and Réka Albert through their work on scale-free networks. In this example, we use our implementation of the GCN algorithm to build a model that predicts citation links in the Cora dataset (see below). I do not want both; rather I want the model to predict the. Never miss an update by subscribing to the weekly Neo4j blog newsletter. mutate( graphName: String, configuration: Map ) YIELD preProcessingMillis: Integer, computeMillis: Integer, postProcessingMillis: Integer, mutateMillis: Integer, relationshipsWritten: Integer, probabilityDistribution: Integer, samplingStats: Map. Any help on this would be appreciated! Attached screenshots. Remove a pipeline from the catalog: CALL gds. I referred to the co-author link prediction tutorial, in that they considered all pair. It may be useful to generate node embeddings with FastRP as a node property step in a machine learning pipeline (like Link prediction pipelines and Node property prediction). The PageRank algorithm measures the importance of each node within the graph, based on the number incoming relationships and the importance of the corresponding source nodes. I have used this to create a new node property. We are dealing with a binary classification problem, where we want to predict if a link exists between a pair of. Link prediction is a common machine learning task applied to graphs: training a model to learn, between pairs of nodes in a graph, where relationships should exist. The Neo4j GraphQL Library is a JavaScript library that can be used with any JavaScript GraphQL implementation, such as Apollo Server. In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. I am trying to follow Mark and Amy's Medium post about link prediction with NEO4J, Link Prediction with NEO4J. Weighted relationships. I am new to AI and ML and interested in application of ML in graph database especially in finance sector. Article Rank. A value of 0 indicates that two nodes are not close, while higher values indicate nodes are closer. Most of the data frames don’t add new information but are repetetive. nodeRegression. I use the run_cypher function, and it works. Check out our graph analytics and graph algorithms that address complex questions. Hi, I resumed the work today and am able to stream my predicted relationships and their probabilities also. Understanding Neo4j GDS Link Predictions (with Demonstration) Let’s explore how Neo4j GDS Link…There are 2 ways of prediction: Exhaustive search, Approximate search. node2Vec computes embeddings based on biased random walks of a node’s neighborhood. You can follow the guides below. NEuler: The Graph Data. In the logs I can see some of the. Introduction to Neo4j Graph Data Science; Neo4j Graph Data Science Fundamentals; Path Finding with GDS;. beta. In addition to the predicted class for each node, the predicted probability for each class may also be retained on the nodes. We’re going to learn how to use the link prediction algorithms with the help of a small friends graph. Option. Neo4j Graph Data Science is a library that provides efficiently implemented, parallel versions of common graph algorithms for Neo4j 3. History and explanation. sensible toseek predictions foredges whose endpoints arenot presentin the traininginterval. Although Neo4j has traditionally been used for transaction workloads, in recent years it is increasingly being used at the heart of graph analytics platforms. A Link Prediction pipeline executes a sequence of steps to compute the features used by a machine learning model. The Neo4j Graph Data Science library includes three different pipelines: node classification, node regression, and link prediction Fig. Link prediction is a common task in the graph context. Since you're still building your model, below - 15871Dear Jennifer, Greetings and hope you are doing well. The release of the Neo4j GDS library version 1. Adding link features. The notebook shows the usage of GDS machine learning pipelines with the Python client and the well-known Cora dataset. You signed in with another tab or window. This visual presentation of the Neo4j graph algorithms is focused on quick understanding and less implementation details. Read More. g. 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. The Neo4j GDS library includes the following community detection algorithms, grouped by quality tier: Production-quality. I am not able to get link prediction algorithms in my graph algorithm library. But again 2 issues here . Although we need negative examples,therefore i use this query to produce links tha doenst exist and because of the complexity i believe that neo4j stop. However, in this post,. Neo4j Desktop comes with a free Developer License of Neo4j Enterprise Edition. So, I was able to train the model and the model is now ready for predictions. We’ll start the series with an overview of the problem and…For the latest guidance, please visit the Getting Started Manual . The exam tests your knowledge of developer-focused concepts, including the graph model, Cypher, and more. 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. 7 and learn how link prediction pipelines can be used to discover travel patterns of digital nomads. Viewing data in familiar chart formats such as bar charts, histograms, pie charts, dials, meters and other representations might be preferred for various users and business needs. ; Emil Eifrem, Neo4j’s CEO, was part of a panel at the virtual SaaStr Annual conference. It has the following use cases: Finding directions between physical locations. Example. . The triangle count of a node is useful as a features for classifying a given website as spam, or non-spam. The categories are listed in this chapter. My objective is to identify the future links between protein and target given positive and negative links. The Closeness Centrality algorithm is a way of detecting nodes that are able to spread information efficiently through a subgraph. Prerequisites. A heterogeneous graph that is used to benchmark node classification or link prediction models such as Heterogeneous Graph Attention Network, MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding and Graph Transformer Networks. In supply chain management, use cases include finding alternate suppliers and demand forecasting. Reload to refresh your session. graph. The Neo4j GDS library includes the following centrality algorithms, grouped by quality tier: Production-quality. Link Predictions in the Neo4j Graph Algorithms Library. Link Prediction algorithms or rather functions help determine the closeness of a pair of nodes. These methods have several hyperparameters that one can set to influence the training. Link Prediction with Neo4j In this week’s Neo4j Online Meetup , Amy Hodler and I presented Link Prediction with Neo4j. If two nodes belong to the same community, there is a greater likelihood that there will be a relationship between them in future, if there isn’t already. End-to-end examples. Link prediction is all about filling in the blanks – or predicting what’s going to happen next. Introduction. Walk through creating an ML workflow for link prediction combining Neo4j and Spark. In this example we consider a graph of products and customers, and we want to find new products to recommend for each customer. On a high level, the link prediction pipeline follows the following steps: Image by the author. While the link parameters for both cases are the same, the URLs are specific to whether you are trying to access server hosted Bloom or Desktop hosted Bloom. It is computed using the following formula: where N (u) is the set of nodes adjacent to u. Yes. This feature is in the beta tier. Set up a database connection for a relational database. Here’s how to train and optimize Link Prediction models in Neo4j Graph Data Science to get the best results. . This stores a trainable pipeline object in the pipeline catalog of type Node classification training pipeline. By clicking Accept, you consent to the use of cookies. Neo4j cloud VMs are based off of the Ubuntu distribution of Linux. Working great until I need to run the triangle detection algorithm: CALL algo. graph. 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. Getting Started Resources. Formulate a link prediction problem in the context of machine learning; Implement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphs; Who this book is for. It is used to predict missing links in the data — either to enrich the data (recommendations) or to. lp_pipe("foo"), or gds. Early control of the related risk factors is crucial to reduce the incidence of DME. Centrality algorithms are used to determine the importance of distinct nodes in a network. The goal of pre-processing is to provide good features for the learning algorithm. UK: +44 20 3868 3223. If not specified, all pipelines in the catalog are listed. Property graph model concepts. We will look into which steps are required to create a link prediction pipeline in a homogenous graph. The pipeline catalog is a concept within the GDS library that allows managing multiple training pipelines by name. Link Prediction Pipelines. The regression model can be applied on a graph in the graph catalog to predict a property value for previously unseen nodes. In this… A Deep Dive into Neo4j Link Prediction Pipeline and FastRP Embedding Algorithm The Link Prediction pipeline combines node properties to generate input features of the Link Prediction model. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. The Louvain method is an algorithm to detect communities in large networks.