Identifying influential users and predicting their ‘‘network impact’’ on social networks have attracted
tremendous interest from both academia and industry. Various definitions of ‘‘influence’’ and many
methods for calculating influence scores have been provided for different empirical purposes and they
often lack the in-depth analysis of the ‘‘characteristics’’ of the output influence. In addition, most of
the developed algorithms and tools are mainly dependent on the static network structure instead of
the dynamic diffusion process over the network, and are thus essentially based on descriptive models
instead of predictive models. Consequently, very few existing works consider the dynamic propagation
of influence in continuous time due to infinite steps for simulation. In this paper, we provide an
evaluation framework to systematically measure the ‘‘characteristics’’ of the influence from the following
three dimensions: (i) Monomorphism vs. Polymorphism; (ii) High Latency vs. Low Latency; and (iii) Information
Inventor vs. Information Spreader. We propose a dynamic information propagation model based on
Continuous-Time Markov Process to predict the influence dynamics of social network users, where the
nodes in the propagation sequences are the users, and the edges connect users who refer to the same
topic contiguously on time. Finally we present a comprehensive empirical study on a large-scale twitter
dataset to compare the influence metrics within our proposed evaluation framework. Experimental
results validate our ideas and demonstrate the prediction performance of our proposed algorithms.
Identifying influential users and predicting their ‘‘network impact’’ on social networks have attractedtremendous interest from both academia and industry. Various definitions of ‘‘influence’’ and manymethods for calculating influence scores have been provided for different empirical purposes and theyoften lack the in-depth analysis of the ‘‘characteristics’’ of the output influence. In addition, most ofthe developed algorithms and tools are mainly dependent on the static network structure instead ofthe dynamic diffusion process over the network, and are thus essentially based on descriptive modelsinstead of predictive models. Consequently, very few existing works consider the dynamic propagationof influence in continuous time due to infinite steps for simulation. In this paper, we provide anevaluation framework to systematically measure the ‘‘characteristics’’ of the influence from the followingthree dimensions: (i) Monomorphism vs. Polymorphism; (ii) High Latency vs. Low Latency; and (iii) InformationInventor vs. Information Spreader. We propose a dynamic information propagation model based onContinuous-Time Markov Process to predict the influence dynamics of social network users, where thenodes in the propagation sequences are the users, and the edges connect users who refer to the sametopic contiguously on time. Finally we present a comprehensive empirical study on a large-scale twitterชุดข้อมูลการเปรียบเทียบวัดอิทธิพลภายในกรอบการประเมินการนำเสนอของเรา ทดลองผลการตรวจสอบความคิดของเรา และแสดงให้เห็นถึงประสิทธิภาพการทำงานของอัลกอริทึมของเรานำเสนอการทำนาย
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