  Understanding how ideas relate to each other is a fundamental question in
many domains, ranging from intellectual history to public communication.
Because ideas are naturally embedded in texts, we propose the first framework
to systematically characterize the relations between ideas based on their
occurrence in a corpus of documents, independent of how these ideas are
represented. Combining two statistics --- cooccurrence within documents and
prevalence correlation over time --- our approach reveals a number of different
ways in which ideas can cooperate and compete. For instance, two ideas can
closely track each other's prevalence over time, and yet rarely cooccur, almost
like a "cold war" scenario. We observe that pairwise cooccurrence and
prevalence correlation exhibit different distributions. We further demonstrate
that our approach is able to uncover intriguing relations between ideas through
in-depth case studies on news articles and research papers.
