Modeling information popularity dynamics based on branching process

Lian-Ren Wu; Jin-Jie Li; Jia-Yin Qi

doi:10.7498/aps.68.20181948

Journals >Acta Physica Sinica >Volume 68 >Issue 7 >Page 078901-1 > Article

Acta Physica Sinica
Vol. 68, Issue 7, 078901-1 (2019)

Modeling information popularity dynamics based on branching process

Lian-Ren Wu¹, Jin-Jie Li^2、*, and Jia-Yin Qi^1、3

Author Affiliations

¹School of Management, Shanghai University of International Business and Economics, Shanghai 201620, China

²College of Mathematics and Science, Shanghai Normal University, Shanghai 200234, China

³Key Laboratory of Trustworthy Distributed Computing and Service, Beijing University of Posts and Telecommunications, Beijing 100876, China

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DOI: 10.7498/aps.68.20181948 Cite this Article

Lian-Ren Wu, Jin-Jie Li, Jia-Yin Qi. Modeling information popularity dynamics based on branching process[J]. Acta Physica Sinica, 2019, 68(7): 078901-1 Copy Citation Text

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Fig. 1. Schematic of the model.流行度模型示意图

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Fig. 2. Distribution of micro-blogs popularity.微博的流行度分布

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Fig. 3. Distribution of out-degree and in-degree.微博用户度分布

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Fig. 4. Mean popularity of Micro-blogs.微博信息平均流行度

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$Complementary cumulative distribution functions (CCDFs)—the fraction of micro-blogs with popularity.微博信息流行度的互补累积概率分布(CCDF)$

Fig. 5. Complementary cumulative distribution functions (CCDFs)—the fraction of micro-blogs with popularity . 微博信息流行度的互补累积概率分布(CCDF)

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列表S	概率	${G_{jk}}\left( {t,\varOmega ;x} \right)$
(a)被新收到的信息所占据	$k\Delta t$	1
(b)创造了一条新的信息	${\mu} \Delta t$	1
(c)以概率 $1 - {\mu} $转发信息	$(1 - {\mu} )\Delta t$	$x{G_{jk}}\left( {t - \Delta t,\varOmega ;x} \right){\left[ {G\left( {t - \Delta t,\varOmega ;x} \right)} \right]^k}$
(d)保持原样	$1 - \left( {k + 1} \right)\Delta t$	${G_{jk}}\left( {t - \Delta t,\varOmega ;x} \right)$

Table 1. Single time-step outcomes of user’s list.

Lian-Ren Wu, Jin-Jie Li, Jia-Yin Qi. Modeling information popularity dynamics based on branching process[J]. Acta Physica Sinica, 2019, 68(7): 078901-1

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