Topic-based influence computation in social networks under resource constraints
Author
Bingöl, Kaan
Advisor
Ferhatosmanoğlu, Hakan
Date
2015Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
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. 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 recent version of the
network maintained locally. Additionally, we propose to use link prediction methods
to further increase 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 (80% higher accuracy for network fetching and 77% for
text fetching) and are superior to state-of-the-art techniques from the literature (21%
higher accuracy).