The chapter discusses the topology and connectivity of the World Wide Web (www), highlighting its complex and unregulated nature. Despite its vast size (estimated at least $8 \times 10^8$ documents), the web's topology remains largely unknown due to its dynamic and ever-changing structure. The authors use local connectivity measurements to construct a topological model of the www, finding that both outgoing and incoming links follow a power-law distribution, distinct from random graph models. This indicates that highly connected web pages dominate the network, and the web forms a small-world network with an average diameter of 19 links. The small diameter suggests that intelligent agents can find desired information quickly, but robots based on string matching need to search a significant fraction of the web to locate information. The scale-free nature of link distributions highlights the importance of collective phenomena in the web's development, emphasizing the need for better understanding and modeling to improve search algorithms and information accessibility.The chapter discusses the topology and connectivity of the World Wide Web (www), highlighting its complex and unregulated nature. Despite its vast size (estimated at least $8 \times 10^8$ documents), the web's topology remains largely unknown due to its dynamic and ever-changing structure. The authors use local connectivity measurements to construct a topological model of the www, finding that both outgoing and incoming links follow a power-law distribution, distinct from random graph models. This indicates that highly connected web pages dominate the network, and the web forms a small-world network with an average diameter of 19 links. The small diameter suggests that intelligent agents can find desired information quickly, but robots based on string matching need to search a significant fraction of the web to locate information. The scale-free nature of link distributions highlights the importance of collective phenomena in the web's development, emphasizing the need for better understanding and modeling to improve search algorithms and information accessibility.