Whether you’re leveraging declared social connections or inferring relationships based on activity, graph databases such as Neo4j offer a world of fresh possibility when it comes to creating innovative social networks or integrating current social graphs into an enterprise application.
Social media networks are already graphs, so there’s no point converting a graph into tables and then back again. Having a data model that directly matches your domain model helps you better understand your data, communicate more effectively and avoid needless work. Using Neo4j improves the quality and speed of development for your social network application by reducing the time you spend data modeling.
How Medium Uses Neo4j
Nathaniel Felsen, DevOps engineer at Medium, talks about how they are using Neo4j for building a social graph and recommendations engine for a personalized content experience.Watch the video
Case Study: LinkedIn China
Discover how Neo4j decreased the development time-to-market for LinkedIn’s Chitu app, a social network for young professionals in China.Read more
What Finance Can Learn from Dating Sites
Learn why dating sites are getting better performance and more value out of their data than financial institutions by using Neo4j to power their social networks.Watch the webinar
Collaboration and sharing
Graphs enable users to connect and share like never before. Make your application more social and interactive for a multitude of worldwide users by tapping into a graph database.
Humans are social creatures, so graph-powered recommendations within a social context are more effective. Friend-of-friend recommendations help users connect and build networks faster and more organically.
Discover unique relationships
Graphs help you understand how people are connected, even when you don’t know their initial relationships.
Faster time to market
Because social networks already function as graphs, developing a social application using a graph database reduces development costs and increases time to market, ultimately allowing for greater revenue generation.
Highly dynamic networks
Social networks change and evolve quickly, so your application must be able to detect early trends and adapt accordingly.
High density of connections
Social networks are densely connected and become more so over time, requiring you to parse this relationship data quickly for better business insights.
Relationships are equally important
When you’re striving to understand user behavior in social networks, relationships between users are as important as the individual users themselves. Your social network application must be able to process data relationships as quickly as it processes individual data entities.
Navigating a social graph and understanding both individuals and their relationships required complex and deep queries. These particular queries bring most relational databases to their knees. Likewise, other types of NoSQL databases struggle to handle high degrees of relatedness. Graph databases are both easy and quick at traversing relationships, and they return instantaneous query results.
Native graph store
Unlike relational databases, Neo4j stores interconnected social data that is neither purely linear nor hierarchical. Neo4j’s native graph storage architecture makes your social network application extremely fast by not forcing intermediate indexing at every turn.
Relationships as first-class entities
Unlike relational databases, where a foreign key constraint denotes only that multiple rows are related, relationships in Neo4j are first-class entities. Each relationship has a type, direction and virtually unlimited number of properties, helping you capture duration, quality and degree-of-influence data.
Neo4j’s versatile property graph model makes it easier for your organization to evolve its data model to accommodate fast-changing social networks.
Performance and scalability
Neo4j’s native graph processing engine supports high-performance graph queries on large datasets to enable real-time decision making.
The built-in, high-availability features of Neo4j ensure your users’ social data is always available for mission-critical applications.
Since its beginning in 2011, the actual data model used by Glowbl was based on a representation of its users through graphs. Find out why Neo4j was a perfect fit for their application.Download the case study
Creating Business Value through Data Relationships
Where does sustainable competitive advantage come from? It’s not from data volume or velocity, but from the knowledge of relationships in your data.Download Now