Multi-layer Graph Analytics for Telecommunication Network Behavior
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Solution Overview
Problem
Current techniques for analyzing and interacting with network behavior in centralized telecommunication networks using graph databases are limited and face challenges in implementation, particularly in wireless communication networks.
Innovation Solution
A method and system that utilize multi-layer graph analytics to receive and process network data from access points, determining intra-layer and inter-layer graph data to iteratively calculate network behavior parameters, enabling proactive and reactive management of network behavior through a central controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional graph database techniques are applied to wireless communication network analysis, then basic anomaly detection and traffic flow analysis can be achieved, but the system lacks comprehensive multi-layer network analysis capability and self-organization capabilities
Solution Approach 1:
The patent segments the network analysis into multiple distinct layers (physical layer, logical layer, behavioral layer) with dedicated graph data structures for each layer. This segmentation allows comprehensive multi-layer analysis while managing complexity by treating each layer independently with its own optimization strategies and data models.
Solution Approach 2:
The patent introduces a temporal dimension by implementing event-driven architectures that process network events in real-time across multiple layers. This dimensional expansion enables the system to analyze not only spatial relationships but also temporal evolution of network behavior, enhancing adaptability without proportionally increasing complexity.
2Reliability
If real-time network data processing is implemented across multiple layers, then network optimization and self-healing capabilities are enhanced, but computational complexity and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing optimized graph data structures, adjacency matrices, and relationship mappings during off-peak periods. This allows the system to quickly retrieve and process pre-prepared network models during real-time operations, enhancing self-healing capability while minimizing processing time delays.
Solution Approach 2:
The patent implements feedback mechanisms where network events trigger iterative graph analytics that continuously update network state models. The system processes events, generates insights, applies corrections, and validates results in closed-loop fashion, enabling rapid self-healing responses while maintaining computational efficiency through incremental updates rather than full re-processing.
3Measurement precision
If comprehensive intra-layer and inter-layer graph analytics are performed, then detailed network behavior parameters can be determined, but computational resources and processing overhead increase
Solution Approach 1:
The patent applies local quality by performing graph analytics at the appropriate level of granularity for each network layer and specific analysis objective. Rather than uniformly processing all network data at maximum detail, the system selectively applies detailed intra-layer analytics only where needed (e.g., for anomaly detection in specific access points) while using coarser inter-layer analytics for overall network trends, thereby maintaining measurement precision where required while reducing overall computational resource consumption.
Data Source
AI summary
This disclosure relates to method and system for analyzing and interacting with network behavior in a centralized telecommunication network. The method includes receiving in real-time, network data from each of a plurality of access points in the telecommunication network; determining intra-layer graph data corresponding to network layers associated with the plurality of access points and inter-layer graph data corresponding to plurality of network layers based on the network data; iteratively determining network behavior parameters corresponding to each of the set of intra-layer connections and each of the set of inter-layer connections based on intra-layer graph data and inter-layer graph data; and generating a multi-layer graphical representation based on the intra-layer graph data, the inter-layer graph data, and the network behavior parameters representing at least one selected multi-relational or multiplex quality of each of relevant intra-layer relationships and inter-layer relationships in the telecommunication network.