Cluster cascades: Infer multiple underlying networks using diffusion data

Yang, Minghao; Chou, Chung‐Kuang; Chen, Ming-Syan

doi:10.1109/asonam.2014.6921597

Cited by 6 publications

(8 citation statements)

References 6 publications

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“…Du et al [3] proposed TopicCascade that infers diffusion networks with topic-dependent transmission rates, but their model infers a cascade's topic distribution from its content. Wang et al (MMRate [27]) and Yang et al (MixCascades [30]) were among the first to build a general framework that infers multilayer diffusion networks solely from spreading data. Later, Liao et al [16] proposed FASTEN that improves the inference accuracy by incorporating a decay parameter in the diffusion model.…”

Section: Related Workmentioning

confidence: 99%

“…However, the range of synthetic test settings, as reported in the papers, is often limited. For example, the tests are usually performed on synthetic networks with fixed numbers of nodes, edges, and layers [16,27,30]. These settings also likely differ from real-world situations in, for example, having independently generated network layers and the assumption that any item spreads on either one layer or another.…”

Section: Related Workmentioning

confidence: 99%

“…5.2.9 Comparison with previous implementations. Finally, we evaluate the performance of our code against existing implemetations of multilayer diffusion network inference, including MixCascades [30], MMRate [27], and FASTEN 6 [16]. We compare the inference accuracy and runtime of the implementations when respectively applied to three synthetic datasets generated under our model, and three other generated under the FASTEN model.…”

Section: 27mentioning

confidence: 99%

“…This heterogeneity can be best represented with a multilayer network [15] where node existence, edge connectivity, and edge weights can differ across layers. Under this framework, researchers have developed methods for inferring multilayer diffusion networks from spreading data [11,12,16,25,27,30].…”

Section: Introductionmentioning

confidence: 99%

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Applicability of Multilayer Diffusion Network Inference to Social Media Data

Xia¹,

Chen²,

Kivelä³

2021

Preprint

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Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, attitude, or language. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns, but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we first introduce an effective implementation of the inference method that can achieve higher accuracy than existing implementations in comparable runtime. Then, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the method, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our findings include that the inference accuracy varies extremely with network density, and that the method fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the method before running the inference on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation supports further testing under personalized settings.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: 27mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applicability of Multilayer Diffusion Network Inference to Social Media Data

Xia¹,

Chen²,

Kivelä³

2021

Preprint

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“…While researchers have developed methods for inferring multilayer diffusion networks from spreading data (Wang et al 2014;Yang, Chou, and Chen 2014;He et al 2015;Liao, Chou, and Chen 2016;He and Liu 2017;Suny et al 2018), these methods have only been tested under limited synthetic settings that likely differ from real-world situations. It is therefore near impossible to conduct empirical analyses using these methods, given the difficulty of assessing the accuracy of their results when applied to real data with unknown ground truth.…”

Section: Introductionmentioning

confidence: 99%

Limits of Multilayer Diffusion Network Inference in Social Media Research

Yan

Chen

Kivelä

2022

ICWSM

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Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, language, or attitude. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns, but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the multilayer diffusion network inference framework, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our results show extreme performance variation of the inference framework: notably, it achieves much higher accuracy when inferring a denser diffusion network, while it fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the inference before running it on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation, which outperforms previous implementations in accuracy, supports further testing under personalized settings.

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