Our names are Mark Needham and Amy Hodler, and we'll be discussing graph algorithms for community detection and recommendations. We'll explore Twitter's social graph, identify its influencers, and understand how to employ various graph algorithms. Specifically, we'll discuss Centrality algorithms like Degree, Closeness, Betweenness, and PageRank, as well as community detection algorithms like the Louvain Modularity algorithm. We'll also cover Cypher projections, which allow us to infer relationships in a graph and run different algorithms on them. Additionally, we'll introduce the Neo4j Graph Algorithms Library, which provides over 45 graph algorithms, including pathfinding, centrality, community detection, link prediction, and similarity algorithms. We'll also discuss how to use these algorithms for propagation pathways, flow and dynamics, and understanding group dynamics. Mark will talk about using graph algorithms in a more practical way, such as analyzing Twitter's social graph, identifying influencers, and running different algorithms on subsets of data. Amy will cover the basics of graph algorithms, including how they differ from querying, when to use them, and how to get started with Neo4j Graph Data Science Library. We'll also explore Cypher projections and NEuler, a React application that allows users to run graph algorithms without writing any Cypher code.