International business machines corporation (20240296324). DIRECTED GRAPH AUTOENCODER DEVICES AND METHODS simplified abstract
Contents
DIRECTED GRAPH AUTOENCODER DEVICES AND METHODS
Organization Name
international business machines corporation
Inventor(s)
Georgios Kollias of White Plains NY (US)
Vasileios Kalantzis of White Plains NY (US)
Tsuyoshi Ide of Harrison NY (US)
Aurelie Chloe Lozano of Scarsdale NY (US)
DIRECTED GRAPH AUTOENCODER DEVICES AND METHODS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296324 titled 'DIRECTED GRAPH AUTOENCODER DEVICES AND METHODS
- Simplified Explanation:**
The patent application describes a directed graph autoencoder device that utilizes a graph convolutional layer to generate transformed dual vector representations of nodes in a graph.
- Key Features and Innovation:**
- Utilizes a graph convolutional layer to transform dual vector representations of nodes in a graph.
- Applies source and target weight matrices to input dual vector representations to generate transformed representations.
- Scales the transformed dual vector representations and performs message passing using the scaled representations.
- Potential Applications:**
- Network analysis
- Social network modeling
- Recommendation systems
- Anomaly detection
- Bioinformatics
- Problems Solved:**
- Efficiently encoding graph data
- Improving representation learning in graph structures
- Enhancing message passing in graph networks
- Benefits:**
- Improved accuracy in graph data representation
- Enhanced performance in graph-based tasks
- Scalability for large graph datasets
- Commercial Applications:**
Graph autoencoder technology can be applied in various industries such as finance, healthcare, marketing, and cybersecurity for tasks like fraud detection, personalized recommendations, and network analysis.
- Prior Art:**
Researchers can explore prior work on graph convolutional networks, autoencoders, and graph embedding techniques to understand the background of this technology.
- Frequently Updated Research:**
Stay updated on advancements in graph neural networks, deep learning for graphs, and graph representation learning to enhance the understanding and application of this technology.
- Questions about Graph Autoencoder Technology:**
1. What are the potential limitations of using graph autoencoder technology in real-world applications? 2. How does the scalability of graph autoencoder models compare to traditional graph processing techniques?
Original Abstract Submitted
a directed graph autoencoder device includes one or more memories and a processor coupled to the one or more memories and configured to implement a graph convolutional layer. the graph convolutional layer comprises a plurality of nodes and is configured to generate transformed dual vector representations by applying a source weight matrix and a target weight matrix to input dual vector representations of the plurality of nodes. the input dual vector representations comprise, for each node of the plurality of nodes, a source vector representation that corresponds to the node in its role as a source and a target vector representation that corresponds to the node in its role as a target. the graph convolutional layer is further configured to scale the transformed dual vector representations to generate scaled transformed dual vector representations. the graph convolutional layer is further configured to perform message passing using the scaled transformed dual vector representations.
