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Social networking and Neo4j

World is full of graph now. Like i know some people, those some people know some other people. Some people like some type of food which other some people may not like and many more.
Now coming to social networks, which 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.

                  

    Unlike relational databases, Neo4j stores interconnected social data that is neither linear nor purely hierarchical. Neo4j’s native graph storing makes your social network application extremely fast by not forcing intermediate indexing at every turn.

    Unlike relational databases, where a foreign key constraint denotes only that multiple rows are related, relationships in Neo4j are first-class citizens. 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 organizations to evolve data models to accommodate fast-changing social networks.

    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 data is always available to mission-critical applications.

Challenges

    Social networks change and evolve quickly, so your application must be able to detect early trends and adapt accordingly.

    Social networks are densely connected and become more so over time, requiring you to parse this relationship data quickly for better business insights.

    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 requires 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, making them an ideal choice for your social application.