Neural Network Generating Node and Edge Embeddings for Graphs
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Solution Overview
Problem
Existing graph neural networks (GNNs) are limited in utilizing edge features and handling heterogeneous nodes and edges, failing to capture complex interactions and relationships in real-world scenarios, such as lead management systems, where tabular data models are insufficient to represent connections between entities.
Innovation Solution
A novel neural network system that models data as a heterogeneous knowledge graph, generating both node and edge embeddings by applying multiple layers to node and edge features, allowing for classification and scoring based on both node and edge features, and enabling handling of disparate entities and evolving graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing graph neural networks are used to process graph data, then node embeddings can be generated, but edge features and heterogeneous relationships are not effectively utilized
Solution Approach 1:
The patent segments the embedding generation process into separate node embedding generation and edge embedding generation pathways. Node embeddings are generated from node features, while edge embeddings are generated from edge features and heterogeneous relationship types. This segmentation allows each pathway to be optimized independently for its specific data type, preventing information loss while maintaining adaptability.
Solution Approach 2:
The patent implements a universal neural network architecture that can handle both homogeneous and heterogeneous graph data through multi-functionality. The network accepts various input types (node features, edge features, relationship types) and processes them through unified layers that adapt to different data characteristics, enabling the system to utilize edge features while maintaining versatility across different graph structures.
2Device complexity
If tabular data models are used to represent entity connections, then simple processing is achieved, but complex interactions and relationships cannot be captured
Solution Approach 1:
The patent transitions from tabular data representation to graph-based representation, adding the dimension of relational structure. Instead of flat tables, the system uses nodes and edges to represent entities and their connections, enabling the capture of complex interactions through multi-dimensional relationships while maintaining manageable complexity through neural network processing.
3Measurement precision
If neural networks utilize both node and edge features, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent merges node embeddings and edge embeddings into a unified representation that combines information from both nodes and edges. This merging occurs through concatenation or summation of embedding vectors, allowing the network to leverage both feature types for improved prediction accuracy while managing computational complexity through efficient vector operations rather than separate processing pipelines.
Data Source
AI summary
A method for using a neural network to generate node embeddings and edge embeddings for graphs. The neural network has K layers. The graph includes multiple nodes and edges linking the multiple nodes. The method includes determining a set of node features for the multiple nodes, and determining a set of edge features for the multiple edges. A first layer of the neural network is applied to the node features and the edge features to output a first set of node embeddings and a first set of edge embeddings. A k-th layer of the neural network is applied to (k−1)th set of node embeddings and (k−1)th set of edge embeddings to output a k-th set of node embeddings and a k-th set of edge embeddings, where the (k−1)th set of node embeddings and (k−1)th set of edge embeddings are output from (k−1)th layer of neural network.