Complex networks have non-trivial characteristics and appear in many real-world systems. Such networks are vitally important, but their full underlying dynamics are not completely understood. Utilizing new data sources, however, can unveil the evolution process of these networks. This study uses the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus to date. We used this dataset to deeply examine the network evolution process, which resulted in two key observations: First, links are more likely to be created among users who join a network at a similar time. Second, the rate in which new users join a network is a central factor in molding a network's topology; i.e., different user-join patterns create different topological properties. Based on these observations, we developed the random network generation model. This model produces not only scale-free networks that have relative high clustering coefficients, but also networks that are sensitive to both the rate and the time in which users join the network. This results in a more accurate and flexible model of how complex networks evolve, one which more closely represents real-world data.
Many real-world complex networks have a community structure in which the division of network nodes into groups within which the network connections are dense, but between which they are sparser'' @cite_0 . In 2004, Newman and Girvan proposed a community detection algorithm and offered a simple method to create networks with community structure @cite_0 .
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2095293504
We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.