  Scientific discovery is shaped by scientists' choices and thus by their
career patterns. The increasing knowledge required to work at the frontier of
science makes it harder for an individual to embark on unexplored paths. Yet
collaborations can reduce learning costs -- albeit at the expense of increased
coordination costs. In this article, we use data on the publication histories
of a very large sample of physicists to measure the effects of knowledge and
social relatedness on their diversification strategies. Using bipartite
networks, we compute a measure of topics similarity and a measure of social
proximity. We find that scientists' strategies are not random, and that they
are significantly affected by both. Knowledge relatedness across topics
explains $\approx 10\%$ of logistic regression deviances and social relatedness
as much as $\approx 30\%$, suggesting that science is an eminently social
enterprise: when scientists move out of their core specialization, they do so
through collaborations. Interestingly, we also find a significant negative
interaction between knowledge and social relatedness, suggesting that the
farther scientists move from their specialization, the more they rely on
collaborations. Our results provide a starting point for broader quantitative
analyses of scientific diversification strategies, which could also be extended
to the domain of technological innovation -- offering insights from a
comparative and policy perspective.
