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As social networks are constantly changing and evolving, methods to analyzedynamic social networks are becoming more important in understanding socialtrends. However, due to the restrictions imposed by the social network serviceproviders, the resources available to fetch the entire contents of a socialnetwork are typically very limited. As a result, analysis of dynamic socialnetwork data requires maintaining an approximate copy of the social network foreach time period, locally. In this paper, we study the problem of dynamicnetwork and text fetching with limited probing capacities, for identifying andmaintaining influential users as the social network evolves. We propose analgorithm to probe the relationships (required for global influencecomputation) as well as posts (required for topic-based influence computation)of a limited number of users during each probing period, based on the influencetrends and activities of the users. We infer the current network based on thenewly probed user data and the last known version of the network maintainedlocally. Additionally, we propose to use link prediction methods to furtherincrease the accuracy of our network inference. We employ PageRank as themetric for influence computation. We illustrate how the proposed solutionmaintains accurate PageRank scores for computing global influence, andtopic-sensitive weighted PageRank scores for topic-based influence. The latterrelies on a topic-based network constructed via weights determined by semanticanalysis of posts and their sharing statistics. We evaluate the effectivenessof our algorithms by comparing them with the true influence scores of the fulland up-to-date version of the network, using data from the micro-bloggingservice Twitter. Results show that our techniques significantly outperformbaseline methods and are superior to state-of-the-art techniques from theliterature.

Topic-Based Influence Computation in Social Networks under Resource Constraints