Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Weighted graphs and disconnected components: patterns and a generator
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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Complex networks of real-world systems are believed to be controlled by common phenomena, producing structures far from regular or random. These include scale-free degree distributions, small-world structure and assortative mixing by degree, which are also the properties captured by different random graph models proposed in the literature. However, many (non-social) real-world networks are in fact disassortative by degree. Thus, we here propose a simple evolving model that generates networks with most common properties of real-world networks including degree disassortativity. Furthermore, the model has a natural interpretation for citation networks with different practical applications.