Graph Network Event Encoding for Low-Latency Node Updates
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Solution Overview
Problem
Conventional machine learning models for node encoding in large online networks suffer from high latency and memory requirements due to the need for re-training and storing node states, making them ineffective in responding to trending events.
Innovation Solution
A node encoding system using generative machine learning models represents events as neural networks, utilizing transformer models and self-attention mechanisms to generate node encodings based on relevant time periods, reducing the need for training and storing node states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional machine learning models are used for node encoding, then the system can process graph network data, but the latency is high and memory usage is high due to re-training requirements and node state storage
Solution Approach 1:
The system pre-computes and stores encodings of graph network evolutions (historical event data) before they are needed for inference. When a node encoding is required, the system retrieves pre-computed encodings and combines them with recent events, avoiding the need for re-training models or processing entire historical datasets in real-time. This preliminary preparation of encoding data significantly reduces inference latency while maintaining encoding quality.
Solution Approach 2:
The patent segments the graph network evolution data into manageable encoding units that can be independently computed and stored. Instead of processing the entire graph network history as a single large dataset, the system divides evolutions into discrete events and time periods, computing encodings for each segment separately. This segmentation enables efficient storage and retrieval operations, reducing both memory requirements and access time during inference.
2Reliability
If conventional machine learning models store node states as parameters, then the model can learn from historical data, but the memory requirements increase significantly
Solution Approach 1:
The patent extracts only the essential encoding representations from historical graph network evolutions, storing these compact encodings rather than the full original data or complete node states. The encoding process transforms large volumes of historical event data into condensed vector representations that capture the essential patterns and relationships. This extraction maintains model accuracy by preserving key information while dramatically reducing the quantity of data that must be stored in memory.
Solution Approach 2:
Instead of storing and processing actual node state data and entire event histories, the system creates encoded copies - compressed numerical representations - of the historical data. These encoding copies retain the essential information needed for accurate node encoding while occupying minimal memory space. The system works with these compact encoding copies during inference rather than with the original large-scale data structures.
3Adaptability or versatility
If the system re-trains models to respond to trending events, then the model can adapt to new patterns, but the processing time and computational resources increase
Solution Approach 1:
The system pre-computes encodings for various graph network evolutions and stores them in advance, organized by event types and time periods. When trending events occur, the system does not need to re-train models from scratch; instead, it retrieves relevant pre-computed encodings and combines them with recent event data. This preliminary preparation enables rapid adaptation to new patterns and trending events without the time cost of re-training, as the computational work has already been performed during encoding generation.
Data Source
AI summary
Methods, systems, and apparatuses include receiving an event notification for an event associated with a node of a graph network. Event data including node state data and a timestamp is generated using the event notification. A node state change is generated for the node by applying a neural network to the node state data and the timestamp. An input sequence for a generative machine learning model is generated, the input sequence including the node state change and the node state data. Updated node state data is computed for the node by applying the generative machine learning model to the input sequence. A node encoding is generated for the node using the updated node state data. Input data for a trained machine learning model is generated using the node encoding. An output of the trained machine learning model is generated by applying the trained machine learning model to the input data.