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So I'm working on a project and I want to use Apache-Age to predict missing links or edges in a grapgh. I've searched through the documentation and forums, but I couldn't find any specific information on link/edge prediction with Apache Age.

Here's a code snippet of what I'm trying to do in Python:

from apache_age import Graph
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

graph = Graph('my_graph')
# populate the graph with nodes and edges

# split the data into training and test sets
train_data, test_data = train_test_split(graph.get_edges(), test_size=0.2)

# train a link prediction model
# (insert machine learning code here)

# make predictions on the test data
predicted_scores = model.predict(test_data)

# evaluate the predictions using ROC AUC score
actual_labels = [graph.has_edge(e[0], e[1]) for e in test_data]
auc_score = roc_auc_score(actual_labels, predicted_scores)
print('ROC AUC score:', auc_score)

Can someone tell me if link prediction is possible with an apache age grapgh. Thank you!

Muhammad Awais Bin Adil
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9 Answers9

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There's no particular method to predict the missing links in the graph by itself since it doesn't even seem logical to predict the missing links in a random graph by itself so you have to build your own algorithm on which missing links can be inserted into the graph by apache age or any other graph database.

Conceptualize and design your own customize algorithm for finding missing links in graph and then implement it through apache age that's the only way possible.

Umer Freak
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Predicting missing links or edges in a graph requires algorithms and techniques specifically designed for that purpose. Apache Age focuses only on graph representation and doesn't include dedicated algorithms for predicting missing links. To do so you will have to utilize machine learning techniques or graph-specific algorithms and then implement it through apacheAGE.

Mohanad Toaima
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As per my understanding, link prediction involves using machine learning algorithms to predict missing links in a graph. Although Apache Age does not have built-in support for link prediction, you can integrate it with different machine-learning libraries to identify the missing links. I hope this will help.

Mahina Sheikh
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Apache Age itself does not provide built-in support for link or edge prediction in graphs. Its primary purpose is to enable efficient querying and processing of graph data using the DataFrame API.Therefore, in your code snippet, you would need to replace the placeholder comment with the actual code from a graph-based machine learning library to train and predict missing links in the graph.

Abdoollaahh
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Generally, link prediction is done using machine learning techniques that use features ang algorithms to predict the required links. An external ML library will be needed to perform link prediction in Apache AGE as apache AGE itself is a graph storage tool.

You can use libraries such as PyTorch, and TensorFlow. First, the data will be split into test and training sets and then the model performs feature extraction which will eventually make the model learn the links and help it to predict the missing links.

Hope this helps!

Haseeb Ashraf
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Link prediction, to my understanding, is the process of predicting missing links in a graph using machine learning techniques. Although link prediction is not supported natively by Apache Age, you can integrate it with various machine-learning tools to find the gaps.

Damil Shahzad
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The primary aim of Apache AGE is efficient processing and representation of graph data which can the be further manipulated for a wide array of purposes. Link prediction is not among the functions performed by the program. It would require you to build a sophisticated AI model to make those predictions according to the data.

Sadeed Ahmad
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Apache Age doesn't inherently include native functionality for predicting links or edges within graphs. Its main objective is to facilitate streamlined querying and manipulation of graph data through the DataFrame API. As a result, in your code, it's necessary to substitute the temporary comment with concrete code sourced from a graph-oriented machine learning library. This code will serve to train the model and forecast absent connections within the graph structure

Damil Shahzad
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Apache Age currently does not include any built-in link/edge prediction feature because it focuses only on its representation.

But you can use Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), or Graph autoencoders for prediction.

You can also convert the graph to NetworkX, which has some builtin link prediction algorithm.

This link has some useful information on using GNNs and NetworkX.

A Comprehensive Introduction to Graph Neural Networks (GNNs)

abhishek2046
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