Quantifying triadic closure in multi-edge social networks

Brandenberger, Laurence; Casiraghi, Giona; Nanumyan, Vahan; Schweitzer, Frank

doi:10.1145/3341161.3342926

Cited by 12 publications

(12 citation statements)

References 29 publications

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“…To compare online and o ine political support among MPs, we use a gHypEG regression model (Casiraghi, 2017;Brandenberger et al, 2019). This is an inferential network model that allows for unbiased estimates of parameters when the independence assumption is violated (Casiraghi et al, 2016(Casiraghi et al, , 2017Casiraghi, 2019).…”

Section: /26mentioning

confidence: 99%

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Why Online does not Equal Offline: Comparing Online and Real-World Political Support Among Politicians.

Brandenberger¹,

Casiraghi²,

Andres³

et al. 2021

Preprint

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Are offline political relations and endorsements established or maintained with online interactions?We examine the external validity of data on political behavior exhibited online and compare it to offline behavior. We overlay two data sets on political support among members of the Swiss parliament to estimate the explanatory power of online endorsements on offline cosponsorship signatures among members, using a gHypEG network regression model.Whereas offline support behavior is driven by a broad range of factors, such as network and homophily effects, online endorsement is less diverse.Our findings show that online endorsement is predominantly driven by partisanship, calling into question the use of online political behavior data to study real-world politics.

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Section: /26mentioning

confidence: 99%

“…where refers to each of shared partners MP and have. Since there are multiple edges between pairs, we take the minimum between and to ensure that only closed triads are counted (see Brandenberger et al, 2019).…”

Section: Control Variablesmentioning

confidence: 99%

Why Online does not Equal Offline: Comparing Online and Real-World Political Support Among Politicians.

Brandenberger¹,

Casiraghi²,

Andres³

et al. 2021

Preprint

Self Cite

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“…To illustrate this, we employ the gHypEG (generalized hypergeometric ensemble of random graphs) [9,12] to model multi-edge network data. The gHypEG allows the encoding of di erent types of hypotheses in a model, from simple ones like block structures [8] to more complex ones, akin to statistical regression models [5,7]. Such models can then be used to evaluate di erent hypotheses about the data [4].…”

Section: Overviewmentioning

confidence: 99%

“…Alternatively, we can specify Ω Ω Ω to encode endogenous network properties. E.g., Ω Ω Ω can be utilized to encode triadic closure [5], to verify whether pairs whose interactions will close triads in the network are more likely than others. Finally, di erent e ects contributing to the odds of observing some interactions instead of others can be composed together to formulate more complex hypotheses [7].…”

Section: Encoding Hypothesesmentioning

confidence: 99%

The likelihood-ratio test for multi-edge network models

Casiraghi

2021

Preprint

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The complexity underlying real-world systems implies that standard statistical hypothesis testing methods may not be adequate for these peculiar applications. Speci cally, we show that the likelihood-ratio test's null-distribution needs to be modi ed to accommodate the complexity found in multi-edge network data. When working with independent observations, the p-values of likelihood-ratio tests are approximated using a 2 distribution. However, such an approximation should not be used when dealing with multi-edge network data. This type of data is characterized by multiple correlations and competitions that make the standard approximation unsuitable. We provide a solution to the problem by providing a better approximation of the likelihood-ratio test null-distribution through a Beta distribution. Finally, we empirically show that even for a small multi-edge network, the standard 2 approximation provides erroneous results, while the proposed Beta approximation yields the correct p-value estimation.

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“…Evidence of triadic closure in primates has been reported by Borgeaud et al (2016) in three groups of vervet monkeys, finding that two individuals are more likely to be associated if they are both linked with a mutual third party associate. The notion of triadic closure is however limited to binary associations (a link is either present or not) and does not consider that links can be weighted or of different types (see however Brandenberger et al (2019) for a recent extension to multi-edge social networks).…”

Section: Introductionmentioning

confidence: 99%

Detecting social (in)stability in primates from their temporal co-presence network

et al. 2019

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The stability of social relationships is important to animals living in groups, and social network analysis provides a powerful tool to help characterize and understand their (in)stability and the consequences at the group level. However, the use of dynamic social networks is still limited in this context because it requires longterm social data and new analytical tools. Here, we study the dynamic evolution of a group of 29 Guinea baboons (Papio papio) using a dataset of automatically collected cognitive tests comprising more than 16M records collected over 3 years. We first built a monthly aggregated temporal network describing the baboon's copresence in the cognitive testing booths. We then used a null model, considering the heterogeneity in the baboons' activity, to define both positive (association) and negative (avoidance) monthly networks. We tested social balance theory by combining these positive and negative social networks. The results showed that the networks were structurally balanced and that newly created edges also tended to preserve social balance. We then investigated several network metrics to gain insights into the individual level and group level social networks long-term temporal evolution. Interestingly, a measure of similarity between successive monthly networks was able to pinpoint periods of stability and instability and to show how some baboons' egonetworks remained stable while others changed radically. Our study confirms the prediction of social balance theory but also shows that large fluctuations in the numbers of triads may limit its applicability to study the dynamic evolution of animal social networks. In contrast, the use of the similarity measure proved to be very versatile and sensitive in detecting relationships' (in)stabilities at different levels. The changes we identified can be linked, at least in some cases, to females changing primary male, as observed in the wild.

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Quantifying triadic closure in multi-edge social networks

Cited by 12 publications

References 29 publications

Why Online does not Equal Offline: Comparing Online and Real-World Political Support Among Politicians.

Why Online does not Equal Offline: Comparing Online and Real-World Political Support Among Politicians.

The likelihood-ratio test for multi-edge network models

Detecting social (in)stability in primates from their temporal co-presence network

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