  As social networks are constantly changing and evolving, methods to analyze
dynamic social networks are becoming more important in understanding social
trends. However, due to the restrictions imposed by the social network service
providers, the resources available to fetch the entire contents of a social
network are typically very limited. As a result, analysis of dynamic social
network data requires maintaining an approximate copy of the social network for
each time period, locally. In this paper, we study the problem of dynamic
network and text fetching with limited probing capacities, for identifying and
maintaining influential users as the social network evolves. We propose an
algorithm to probe the relationships (required for global influence
computation) as well as posts (required for topic-based influence computation)
of a limited number of users during each probing period, based on the influence
trends and activities of the users. We infer the current network based on the
newly probed user data and the last known version of the network maintained
locally. Additionally, we propose to use link prediction methods to further
increase the accuracy of our network inference. We employ PageRank as the
metric for influence computation. We illustrate how the proposed solution
maintains accurate PageRank scores for computing global influence, and
topic-sensitive weighted PageRank scores for topic-based influence. The latter
relies on a topic-based network constructed via weights determined by semantic
analysis of posts and their sharing statistics. We evaluate the effectiveness
of our algorithms by comparing them with the true influence scores of the full
and up-to-date version of the network, using data from the micro-blogging
service Twitter. Results show that our techniques significantly outperform
baseline methods and are superior to state-of-the-art techniques from the
literature.
