Presentation Link: SCTE TechExpo 2025 Masterclass

Introduction

At this year’s Society of Cable Telecommunications Engineers (SCTE) TechExpo, I had the opportunity to present on Graph Neural Networks (GNNs) and their applications in the cable industry. Our masterclass covered graph fundamentals, practical algorithms for network troubleshooting, and advanced machine learning techniques for understanding network topology.

The session included:

  • A crash course in graphs and graph algorithms
  • Least Common Ancestor algorithms for locating network impairments
  • An introduction to GNNs for understanding latent structures in HFC networks
  • Graph-based approaches to organizing work tickets and alarms

I handled the GNN portion of the presentation, and this post serves as a primer for that material.

The Fundamental Problem with Graphs and Classical Deep Learning

Many machine learning problems involve data that isn’t inherently Euclidean. Non-Euclidean data, including graphs and certain manifolds, lacks the natural distance metrics we can embed in n-dimensional Euclidean space. For instance, in graph structures, “distance” might mean hop count between nodes or weighted edge traversals, which fundamentally changes how we approach deep learning.

Traditional deep learning models expect:

  • Natural ordering (like pixels in an image)
  • Fixed dimensionality (like tabular data)
  • Translation invariance

Graphs don’t provide these properties:

  • Nodes have no natural ordering
  • Graph size varies
  • There’s no ambient space—the graph is the space

Consider feeding a graph’s adjacency matrix into a standard Convolutional Neural Network (CNN). You immediately run into problems: reordering the nodes produces different outputs because the model isn’t permutation invariant. Graphs vary in size, so there’s no way to “stretch” them to match dimensions across a dataset. Most critically, CNNs treat position as meaningful, but in graphs, what matters is the relationships between nodes, not their position in the adjacency matrix.

Why We Need GNNs

Graph Neural Networks operate on the latent geometry of graphs, meaning the architecture conforms to the natural relationships between nodes. GNNs use message passing layers that aggregate information from neighbors across the entire graph, preserving inherent relationships and capturing long-range dependencies.

These aggregation functions, such as SUM, MAX and MEAN, are permutation invariant, so the order in which nodes are processed doesn’t matter. While GNNs involve considerably more complexity than I’m covering here, this captures the essential motivation for their design.

Applications in Telecommunications

Telecommunications networks are inherently graph-structured. In Hybrid Fiber Coax (HFC) networks, modulated signals encoding data pass through various devices that maintain signal strength before reaching the modem for demodulation. We can model these networks with devices as nodes and cable connections as edges.

This model captures long-range influences: device issues can propagate both upstream and downstream, affecting distant parts of the network through neighboring devices. GNNs can learn from this neighbor information to identify subtle, complex patterns.

We applied GNNs to detecting amplifier cascades within HFC networks. With limited training data, just network graphs and labels, the models successfully learned distinguishing features of amp cascades without manual feature engineering. This demonstrates that GNNs can discover important features autonomously, reducing the need for time-consuming and error-prone human labeling.

At scale, this approach could help identify high-impact features, nodes, and edges within telecommunications networks, streamlining network analysis and maintenance. Though our work here was primarily a proof of concept for the masterclass.

Wrap-Up

This post covers the GNN portion of our SCTE presentation. My colleagues addressed the graph fundamentals and practical algorithms that bookended this section. For the complete picture, check out the full presentation materials at the link below.

Full Presentation: SCTE TechExpo 2025 Masterclass