Advanced Graph Analysis

CoreRec provides cutting-edge tools for analyzing complex graph structures. These tools are designed to help data scientists and researchers understand the intricate relationships within their data, enabling more informed decision-making and insights.

import core_rec as cr
graph = cr.load_graph("path/to/graph")
analysis_results = cr.analyze_graph(graph)
                        

Node Recommendation Engine

Utilize CoreRec's powerful engine to recommend similar nodes within a graph. This feature enhances user experience and engagement by providing personalized recommendations based on graph data.

import core_rec as cr
recommendations = cr.recommend_similar_nodes(graph, node_id)
                        

Customizable Transformer Model

Define and train Transformer models tailored to your graph data with customizable parameters. This allows for optimal performance in various graph-related tasks such as node classification or link prediction.

import core_rec as cr
model = cr.GraphTransformer(num_layers=2, d_model=128, num_heads=4, d_feedforward=512, input_dim=10)
                        

PyTorch Dataset Integration

Seamlessly integrate graph data with PyTorch datasets, streamlining the model training process. This feature simplifies the preparation of graph data for machine learning tasks.

import core_rec as cr
dataset = cr.GraphDataset(adj_matrix)
data_loader = DataLoader(dataset, batch_size=32, shuffle=True)
                        

Flexible Model Training

Train your models with ease using CoreRec's flexible training functions. These functions support various configurations, making it easier to adapt to different training requirements.

import core_rec as cr
cr.train_model(model, data_loader, criterion, optimizer, num_epochs=10)
                        

Accurate Recommendation Metrics

Measure the accuracy of your recommendations with robust metrics provided by CoreRec. This feature ensures that your recommendation system is performing optimally.

import core_rec as cr
accuracy = cr.aaj_accuracy(graph, node_index, recommended_indices)
                        

2D Graph Visualizations

Create stunning 2D visualizations of your graphs, making data analysis more intuitive and insightful. This feature helps in better understanding the structure and relationships within the graph.

import vish_graphs as vg
vg.draw_graph(adj_matrix, nodes, top_nodes)
                        

3D Graph Visualizations

Experience your graphs in 3D with customizable features, providing a deeper understanding of complex networks. This feature is particularly useful for visualizing large and intricate graph structures.

import vish_graphs as vg
vg.draw_graph_3d(adj_matrix, nodes, top_nodes)
                        

Full Documentation

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