SNA Metrics

Home » SNA Metrics
Updated on

Introduction

Accurately measuring network influence on mobile requires real Android environments. Traditional browser-based simulations or emulators can distort Social Network Analysis (SNA) measurements due to incomplete device fingerprints and restricted app behaviors. GeeLark addresses this challenge by provisioning fully isolated Android instances in the cloud, each with hardware-backed device identifiers. This setup ensures that data collected through actual app interactions is representative of real-world user behavior, improving the validity of SNA metrics across mobile networks.

Core SNA Metrics

Social Network Analysis metrics quantify structural and dynamic patterns in networks of devices or users:

  • Degree centrality: number of direct connections per node.
  • Betweenness centrality: frequency at which a node lies on shortest paths.
  • Closeness centrality: average distance from a node to all others.
  • Eigenvector centrality: influence based on connections to high-scoring nodes.
  • Density: ratio of existing ties to all possible ties.
  • Clustering coefficient: likelihood that a node’s neighbors are connected.

These metrics reveal influencers, information pathways, subgroup structures, and network vulnerabilities.

GeeLark Platform Overview

GeeLark’s architecture leverages cloud-hosted Android environments to capture authentic app-level network data. Each environment replicates genuine hardware fingerprints, allowing unmodified mobile applications to execute in parallel with full system-level access. This eliminates limitations of browser-based antidetect solutions by enabling direct measurement of inter-app communication, API calls, and network traffic. All data processing occurs under strict privacy controls inherent in the cloud infrastructure.

Mobile Marketing Attribution

In mobile marketing, understanding user journeys across multiple apps is critical for accurate attribution. GeeLark tracks app-to-app transitions within isolated Android environments and applies centrality metrics to uncover key touchpoints and hidden influencers.

• User paths traced across 200+ apps in cloud instances.
• Betweenness centrality highlights critical conversion steps.
• Eigenvector centrality quantifies each channel’s relative influence.

App Store Ecosystem Analysis

App discovery networks on platforms like Google Play exhibit complex co-installation and referral patterns.

  • Relationship Graphing:
    Degree centrality maps co-installation counts.
    Betweenness centrality traces referral pathways.
    Eigenvector centrality spots category bridges.
  • Community Detection:
    Clustering coefficients uncover affinity clusters.
    Density metrics gauge category saturation.

Analysis of Google Play Store data indicated that games with high betweenness centrality retained 28% more users and that the top 5% of eigenvector-central apps drove 61% of referrals.

Key Takeaways:
• sna metrics pinpoint influential app clusters for targeted campaigns.
• Betweenness analysis predicts app retention trends.
• Eigenvector insights drive referral marketing strategies.

SKAdNetwork Integration

Apple’s SKAdNetwork (SKAN) restricts granular attribution data.

  • Network Reconstruction:
    Infers device connections from aggregated conversion values.
    Estimates centrality measures with Monte Carlo simulations.
  • Campaign Optimization:
    Allocates budgets based on inferred node influence.
    Targets clusters with high estimated eigenvector scores.

In SKAN-compliant tests, probabilistic sna achieved 89% accuracy in predicting high-value networks and improved campaign efficiency by 17% versus rule-based heuristics.

Key Takeaways:
• Probabilistic modeling recovers sna insights under SKAN limitations.
• Estimated centrality guides budget allocation in privacy-first contexts.
• Federated analytics preserve privacy while enabling cross-campaign comparison.

Conclusion

By integrating sna metrics with authentic cloud-hosted Android environments, GeeLark enhances the accuracy and applicability of network analysis in mobile marketing.

People Also Ask

What is the SNA methodology?

The SNA methodology systematically studies social structures by treating individuals or entities (nodes) and their relationships (edges). It involves:

• Data collection: Gather relational data such as communication logs, collaboration records or affiliations.
• Network modeling: Define nodes, ties and relevant attributes.
• Metric computation: Calculate measures like degree, betweenness, closeness and clustering coefficients.
• Visualization: Create graphs to reveal communities, bridges and key influencers.
• Interpretation: Relate structural patterns to behavioral, organizational or diffusion outcomes.
• Iteration: Refine data and models based on new insights or validation needs.

What are the 4 measures of centrality?

The four common centrality measures are:

• Degree centrality: counts direct connections.
• Betweenness centrality: measures control over shortest‐path communication.
• Closeness centrality: captures proximity to all others via shortest paths.
• Eigenvector centrality: evaluates influence based on connections to highly connected nodes.

What are the metrics of social network analysis?

Common SNA metrics include:

• Degree centrality: number of direct connections a node has
• Betweenness centrality: frequency a node lies on shortest paths
• Closeness centrality: inverse of average distance to all others
• Eigenvector centrality: influence based on connections to well-connected nodes
• Network density: ratio of existing ties to all possible ties
• Clustering coefficient: likelihood neighbors of a node are interconnected
• Average path length and diameter: typical and maximum distances between nodes
• Reciprocity: proportion of mutual ties
• Modularity: strength of community or cluster structure

What is SNA used for?

Social Network Analysis (SNA) is used to:

• Identify key influencers and opinion leaders
• Detect communities, clusters or subgroups
• Map and visualize relationship patterns
• Analyze information or disease diffusion pathways
• Understand organizational structures and collaboration
• Optimize marketing and outreach strategies
• Investigate criminal or terrorist networks
• Guide public health interventions
• Improve team performance and communication dynamics
• Inform policy making by revealing social cohesion and structural gaps

Related Posts

  1. Network Management
    In the fast-evolving digital landscape, network management plays a crucial role for businesses seeking to achieve robust performance, security, and reliability within their technology infrastructures....
  2. Device Ping
    The ping command is a fundamental network diagnostic tool used to test connectivity between devices by sending ICMP (Internet Control Message Protocol) echo requests and...
  3. Network Traffic
    Introduction to Network Traffic Network traffic is the flow of data packets across computer networks, encompassing web requests, file transfers, emails, streaming media and more....
  4. Network latency
    Network latency is a critical factor that can make or break the performance of cloud-based systems, especially in the realm of mobile and phone automation....
  5. Network state
    Network state management is essential for ensuring a strong and efficient connectivity experience. It encompasses monitoring, controlling, and optimizing network performance to facilitate seamless user...