The paper presents a hierarchical agglomeration algorithm for detecting community structure in large networks, which is significantly faster than many competing methods. The algorithm's running time is \(O(m d \log n)\), where \(m\) is the number of edges, \(n\) is the number of vertices, and \(d\) is the depth of the dendrogram describing the community structure. For sparse and hierarchical networks, this reduces to essentially linear time, \(O(n \log^2 n)\). The authors demonstrate the algorithm's effectiveness by analyzing a network of items for sale on Amazon.com, revealing meaningful communities that correspond to specific topics or genres. The algorithm's efficiency allows for the analysis of extremely large networks, making it a valuable tool for researchers in various fields.The paper presents a hierarchical agglomeration algorithm for detecting community structure in large networks, which is significantly faster than many competing methods. The algorithm's running time is \(O(m d \log n)\), where \(m\) is the number of edges, \(n\) is the number of vertices, and \(d\) is the depth of the dendrogram describing the community structure. For sparse and hierarchical networks, this reduces to essentially linear time, \(O(n \log^2 n)\). The authors demonstrate the algorithm's effectiveness by analyzing a network of items for sale on Amazon.com, revealing meaningful communities that correspond to specific topics or genres. The algorithm's efficiency allows for the analysis of extremely large networks, making it a valuable tool for researchers in various fields.