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Temporal scaling in information propagation.

Huang J, Li C, Wang WQ, Shen HW, Li G, Cheng XQ - Sci Rep (2014)

Bottom Line: Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks.In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website.Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.

View Article: PubMed Central - PubMed

Affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China.

ABSTRACT
For the study of information propagation, one fundamental problem is uncovering universal laws governing the dynamics of information propagation. This problem, from the microscopic perspective, is formulated as estimating the propagation probability that a piece of information propagates from one individual to another. Such a propagation probability generally depends on two major classes of factors: the intrinsic attractiveness of information and the interactions between individuals. Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks. In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website. We discover a temporal scaling in information propagation: the probability a message propagates between two individuals decays with the length of time latency since their latest interaction, obeying a power-law rule. Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.

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Related in: MedlinePlus

Characterizing propagation probabilities.(a,b) Time stamps of positive examples (retweeting behaviors) on two random edges. Each vertical line represents a retweeting behaviors occurring with the time stamp marked on the horizontal axis. (c,d) Positive (retweeting) and negative (neglecting) examples on those two edges. Vertical lines in upper half represent positive examples, while those in lower half represent negative ones. It shows an obvious tendency that most positive examples are concentrated on the left zone, i.e., most retweeting behaviors occur with short latency. The tendency is stronger on (c) than that on (d). (e) Distribution of latency of retweeting behaviors over all edges. (f) Ratio of positive examples upon all examples on all edges with respect to the associated latency, demonstrating the power-law interdependence between the propagation probability and the latency.
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f1: Characterizing propagation probabilities.(a,b) Time stamps of positive examples (retweeting behaviors) on two random edges. Each vertical line represents a retweeting behaviors occurring with the time stamp marked on the horizontal axis. (c,d) Positive (retweeting) and negative (neglecting) examples on those two edges. Vertical lines in upper half represent positive examples, while those in lower half represent negative ones. It shows an obvious tendency that most positive examples are concentrated on the left zone, i.e., most retweeting behaviors occur with short latency. The tendency is stronger on (c) than that on (d). (e) Distribution of latency of retweeting behaviors over all edges. (f) Ratio of positive examples upon all examples on all edges with respect to the associated latency, demonstrating the power-law interdependence between the propagation probability and the latency.

Mentions: We start to explore the temporal scaling of information propagation by examining time stamps of positive examples on two randomly selected edges, a followee and two of his followers. Figure 1a and Figure 1b reveal a non-uniform density of positive examples that the followers frequently retweet messages from the followee in several short time periods, separated by long idle periods. This implies a burst phenomenon on individual interactions: short time frames of intense interactions are separated by long idle periods35. To provide a solid evidence for the existence of burst in retweeting behaviors, we depict in Figure 1e the distribution of latency of all positive examples. The power-law distribution of latency, reflecting the emergence of bursty retweeting behaviors, exhibits the temporal nature of individual interactions. Note that static individual interactions lead to a time-invariant propagation probability on each edge in this scenario, which views retweeting behaviors as a homogeneous Poisson process, resulting in an exponential distribution of latency.


Temporal scaling in information propagation.

Huang J, Li C, Wang WQ, Shen HW, Li G, Cheng XQ - Sci Rep (2014)

Characterizing propagation probabilities.(a,b) Time stamps of positive examples (retweeting behaviors) on two random edges. Each vertical line represents a retweeting behaviors occurring with the time stamp marked on the horizontal axis. (c,d) Positive (retweeting) and negative (neglecting) examples on those two edges. Vertical lines in upper half represent positive examples, while those in lower half represent negative ones. It shows an obvious tendency that most positive examples are concentrated on the left zone, i.e., most retweeting behaviors occur with short latency. The tendency is stronger on (c) than that on (d). (e) Distribution of latency of retweeting behaviors over all edges. (f) Ratio of positive examples upon all examples on all edges with respect to the associated latency, demonstrating the power-law interdependence between the propagation probability and the latency.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4061555&req=5

f1: Characterizing propagation probabilities.(a,b) Time stamps of positive examples (retweeting behaviors) on two random edges. Each vertical line represents a retweeting behaviors occurring with the time stamp marked on the horizontal axis. (c,d) Positive (retweeting) and negative (neglecting) examples on those two edges. Vertical lines in upper half represent positive examples, while those in lower half represent negative ones. It shows an obvious tendency that most positive examples are concentrated on the left zone, i.e., most retweeting behaviors occur with short latency. The tendency is stronger on (c) than that on (d). (e) Distribution of latency of retweeting behaviors over all edges. (f) Ratio of positive examples upon all examples on all edges with respect to the associated latency, demonstrating the power-law interdependence between the propagation probability and the latency.
Mentions: We start to explore the temporal scaling of information propagation by examining time stamps of positive examples on two randomly selected edges, a followee and two of his followers. Figure 1a and Figure 1b reveal a non-uniform density of positive examples that the followers frequently retweet messages from the followee in several short time periods, separated by long idle periods. This implies a burst phenomenon on individual interactions: short time frames of intense interactions are separated by long idle periods35. To provide a solid evidence for the existence of burst in retweeting behaviors, we depict in Figure 1e the distribution of latency of all positive examples. The power-law distribution of latency, reflecting the emergence of bursty retweeting behaviors, exhibits the temporal nature of individual interactions. Note that static individual interactions lead to a time-invariant propagation probability on each edge in this scenario, which views retweeting behaviors as a homogeneous Poisson process, resulting in an exponential distribution of latency.

Bottom Line: Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks.In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website.Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.

View Article: PubMed Central - PubMed

Affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China.

ABSTRACT
For the study of information propagation, one fundamental problem is uncovering universal laws governing the dynamics of information propagation. This problem, from the microscopic perspective, is formulated as estimating the propagation probability that a piece of information propagates from one individual to another. Such a propagation probability generally depends on two major classes of factors: the intrinsic attractiveness of information and the interactions between individuals. Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks. In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website. We discover a temporal scaling in information propagation: the probability a message propagates between two individuals decays with the length of time latency since their latest interaction, obeying a power-law rule. Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.

Show MeSH
Related in: MedlinePlus