Summary
In this chapter, we covered the essentials of graph theory, a branch of mathematics that studies graphs and networks. We began by defining what a graph is and explained the different types of graphs, such as directed, weighted, and connected graphs. We then introduced fundamental graph objects (including neighbors) and measures (such as centrality and density), which are used to understand and analyze graph structures.
Additionally, we discussed the adjacency matrix and its different representations. Finally, we explored the two fundamental graph algorithms, BFS and DFS, which form the foundation for developing more complex graph algorithms.
In Chapter 3, Creating Node Representations with DeepWalk, we will explore the DeepWalk architecture and its two components: Word2Vec and random walks. We will start by understanding the Word2Vec architecture and then implement it using a specialized library. Then, we will delve into the DeepWalk algorithm and implement random walks...
