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SCIENCE CHINA Information Sciences, Volume 61, Issue 9: 092101(2018) https://doi.org/10.1007/s11432-016-9122-x

Inferring diffusion networks with life stage heterogeneity

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  • ReceivedJul 18, 2016
  • AcceptedMay 10, 2017
  • PublishedNov 2, 2017

Abstract

A network inference problem focuses on discovering the structure of a diffusion network from observed cascades. This problem is significantly more challenging inseveral settings in which this type of an inference is desirable or necessarybecause of heterogeneity in the diffusion process. The heterogeneity of the diffusion process in different life stages results in the inaccuracy of acommon assumption of constant influence strength. In this study, a Life Stage HeuristicLSH)method is proposed to model life stage heterogeneity by decoupling the popularity level of anitem under propagation from a true strength of social ties to improve inference accuracy. The proposed LSH is incorporated into almost allexisting state-of-the-art network inference algorithms to improve estimation accuracy with only minimal changes in the implementationand maintaining the same running time. Additionally, NetRate NetInf and ConNIeare used as three examples to demonstrate the power of the proposed method. Furthermore, clustering of cascades prior to the LSH is proposed to eliminate noise, and the optimized method is termed as Clustered Life Stage HeuristicCLSH). Extensive experiments on synthetic and real world datasets indicate that both LSH and CLSH methods significantly improve the accuracy of network inference.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61572041), Beijing Natural Science Foundation (Grant No. 4152023), and National High Technology Research and Development Program of China (863 Program) (Grant No. 2014AA015103).


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  • Figure 1

    (Color online) The diffusion speed in different stages of diffusion processes in the Sina microblog dataset and US Patent dataset. (a) Cascade A in Sina dataset; (b) cascade B in Sina dataset; (c) cascade A in Patent dataset; protectłinebreak (d) cascade B in Patent dataset.

  • Figure 2

    (Color online) Performance of (C)LSH-NetRate (a), (C)LSH-ConNIe (b), (C)LSH-NetInf (c) with respect to various network structures. The $x$-axis denotes different synthetic network structures.

  • Figure 3

    (Color online) Performance of (C)LSH-NetRate (a), (C)LSH-ConNIe (b), (C)LSH-NetInf (c) with respect to different numbers of cascades. The $x$-axis denotes a different number of cascades.

  • Figure 4

    (Color online) Performance of (C)LSH-NetRate (a), (C)LSH-ConNIe (b), (C)LSH-NetInf (c) with respect to different heterogeneity levels. The $x$-axis denotes different levels of heterogeneity.

  • Figure 5

    (Color online) Performance on the likelihood function maximum. (a) (C)LSH-NetRate; (b) (C)LSH-ConNIe.

  • Figure 6

    (Color online) Performance with respect to different parameter values $L$ and $K$. (a) Max F1 score on various $L$; (b) AUC on various $L$; (c) max F1 score on various $K$; (d) AUC on various $K$.

  • Figure 7

    (Color online) Precision-recall curve with respect to MemeTracker dataset. (a) (C)LSH-NetRate; (b) (C)LSH-ConNIe; (c) (C)LSH-NetInf.

  • Figure 8

    (Color online) Precision-recall curve with respect to Patent dataset. (a) (C)LSH-NetRate; (b) (C)LSH-ConNIe; (c) (C)LSH-NetInf.

  • Figure 9

    (Color online) Performance on MemeTracker dataset vs. the number of cascades. (a) (C)LSH-NetRate; protectłinebreak (b) (C)LSH-ConNIe; (c) (C)LSH-NetInf.

  • Figure 10

    (Color online) Performance on Patent dataset vs. the number of cascades. (a) (C)LSH-NetRate; (b) (C)LSH-ConNIe; (c) (C)LSH-NetInf.

  • Figure 11

    (Color online) The popularity level estimated by CLSH in MemeTracker and Patent datasets. (a) Cluster A in MemeTracker; (b) cluster B in MemeTracker; (c) cluster A in Patent dataset; (d) cluster B in Patent dataset.

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