  Networks describe various complex natural systems including social systems.
We investigate the social network of co-occurrence in Reuters-21578 corpus,
which consists of news articles that appeared in the Reuters newswire in 1987.
People are represented as vertices and two persons are connected if they
co-occur in the same article. The network has small-world features with
power-law degree distribution. The network is disconnected and the component
size distribution has power law characteristics. Community detection on a
degree-reduced network provides meaningful communities. An edge-reduced
network, which contains only the strong ties has a star topology.
  "Importance" of persons are investigated. The network is the situation in
1987. After 20 years, a better judgment on the importance of the people can be
done. A number of ranking algorithms, including Citation count, PageRank, are
used to assign ranks to vertices. The ranks given by the algorithms are
compared against how well a person is represented in Wikipedia. We find up to
medium level Spearman's rank correlations. A noteworthy finding is that
PageRank consistently performed worse than the other algorithms. We analyze
this further and find reasons.
