 
Summary: Optimally Learning Social Networks with
Activations and Suppressions
Dana Angluin and James Aspnes Lev Reyzin
Department of Computer Science, Yale University
{angluin,aspnes}@cs.yale.edu, lev.reyzin@yale.edu
Abstract. In this paper we consider the problem of learning hidden
independent cascade social networks using exact value injection queries.
These queries involve activating and suppressing agents in the target net
work. We develop an algorithm that optimally learns an arbitrary social
network of size n using O(n2
) queries, matching the information theo
retic lower bound we prove for this problem. We also consider the case
when the target social network forms a tree and show that the learn
ing problem takes (n log(n)) queries. We also give an approximation
algorithm for finding an influential set of nodes in the network, without
resorting to learning its structure. Finally, we discuss some limitations
of our approach, and limitations of pathbased methods, when nonexact
value injection queries are used.
1 Introduction
Social networks are used to model interactions within populations of indi
