An experimental project that explores the power of social data. It builds a network of relationships between users by accessing their phone contacts and analyzing connection patterns. The result is a fascinating visualization of how people are interconnected through their contact lists. By analyzing the data we can draw conclusions about how people are related.

The concept behind Phonebook is simple yet powerful: by analyzing who has each other in their contacts, we can map out social connections and discover patterns that would otherwise remain hidden. The project creates a visual graph representation of these connections, allowing users to explore their place in the broader social network.

Interactive graph visualization of contact networks

Privacy-focused design with user consent for contact sharing

Discover mutual connections and relationship patterns

Identify clusters and communities within your network

Visualize strength of connections based on communication frequency

Search and explore your extended network

Explore degrees of separation between individuals

Phonebook uses Go for its backend services, providing excellent performance and ease of development. The frontend is built with React for a responsive and interactive user experience. Data is stored in ArangoDB, a powerful graph database that efficiently manages the complex relationships between contacts.

The visualization component uses Canvas for rendering the network graphs, allowing for smooth interaction even with thousands of nodes. Nodes are streamed in as the network is explored and only rendered if necessary. The application employs custom graph traversal algorithms to identify meaningful patterns and communities within the contact network.

Privacy is a central concern for Phonebook. The application is designed with a strict opt-in model, requiring explicit consent before any contact information is processed. All data is encrypted, and users have complete control over what information is shared and with whom. The project aims to demonstrate the value of social data while respecting privacy boundaries.