  Identifying influential nodes that can jointly trigger the maximum influence
spread in networks is a fundamental problem in many applications such as viral
marketing, online advertising, and disease control. Most existing studies
assume that social influence is static and they fail to capture the dynamics of
influence in reality. In this work, we address the dynamic influence challenge
by designing efficient streaming methods that can identify influential nodes
from highly dynamic node interaction streams. We first propose a general
time-decaying dynamic interaction network (TDN) model to model node interaction
streams with the ability to smoothly discard outdated data. Based on the TDN
model, we design three algorithms, i.e., SieveADN, BasicReduction, and
HistApprox. SieveADN identifies influential nodes from a special kind of TDNs
with efficiency. BasicReduction uses SieveADN as a basic building block to
identify influential nodes from general TDNs. HistApprox significantly improves
the efficiency of BasicReduction. More importantly, we theoretically show that
all three algorithms enjoy constant factor approximation guarantees.
Experiments conducted on various real interaction datasets demonstrate that our
approach finds near-optimal solutions with speed at least $5$ to $15$ times
faster than baseline methods.
