Modeling of Growing Networks with Directional Attachment and Communities

摘要

With the aim of acquiring a more precise probabilistic model for the future graph structure of such a real-world growing network as the Web, we propose a new network growth model and its learning algorithm. Unlike the conventional models, we have incorporated directional attachment and community structure for this purpose. We formally show that the proposed model also exhibits a degree distribution with a power-law tail. Using the real data of Web pages on the topic "mp3", we experimentally show that the proposed method can more precisely predict the probability of a new link creation in the future.