There’s a hidden world of valuable data existing as invisible relationships within social networks. By ignoring the subsurface connections between seemingly unrelated content, these networks are missing out on a huge opportunity.
Developers can generate valuable graph-based recommendations by harnessing the power of a graph database to store, manage and query the invisible relationships between people, brands, interests and more.
In this session, Kenny Bastani, Neo4j Developer Evangelist, will demonstrate how graph databases are the key to providing richer experiences through personalized online interactions and content discovery.
27. Demand
• Describing the problem and some challenges
• What are invisible relationships?
• How do you infer relationships using a graph data model?
• How do you graph the demand for social content?
• How do you distribute valuable content where it is most demanded?