Star Search: Effective Subgroups In Collaborative Social Networks

In an ever-increasing variety of contexts, people are working collaboratively to solve problems and accomplish tasks. Yet the characteristics of teams that work effectively are not fully understood. We focus on the problem of identifying particularly effective teams in a large, complex, social network. More specifically, given some task, and taking into account measures of both the effectiveness of an individual and the strength of the pairwise ties between individuals, can we identify the subgroup of people who are most likely to accomplish said task? Our experimental work using the DBLP suggests that within any given subgroup, not all ties are of equal relevance. In fact, we show that focusing on the ties between one individual and the other group members will suffice. However, whereas the problem of finding the "best" subgroup is equivalent to MAX-CLIQUE and is thus hard to approximate, the problem of finding the best star is computationally tractable. We present experimental evidence justifying interest in the star, as opposed to the clique, and discuss algorithmic and complexity concerns.