Community detection and reciprocity in networks by jointly modeling pairs of edges

Contisciani, Martina; Safdari, Hadiseh; Bacco, Caterina De

doi:10.48550/arxiv.2112.10436

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…As accurately identifying anomalies is deeply connected with the chosen null model determining what regular patterns are, it is important to consider other possible mechanism for tie formation, beyond community structure. In recent works [31][32][33], we found that modeling community patterns together with reciprocity effects, leads to higher predictive performance, thus more expressive generative models. This could significantly change the performance of our foundational model as well.…”

Section: Discussionmentioning

confidence: 86%

Anomaly detection and community detection in networks

Safdari¹,

Bacco²

2022

Preprint

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Anomaly detection is a relevant problem in the area of data analysis. In networked systems, where individual entities interact in pairs, anomalies are observed when pattern of interactions deviates from patterns considered regular. Properly defining what regular patterns entail relies on developing expressive models for describing the observed interactions. It is crucial to address anomaly detection in networks. Among the many well-known models for networks, latent variable models -a class of probabilistic models -offer promising tools to capture the intrinsic features of the data. In this work, we propose a probabilistic generative approach which incorporates domain knowledge, i.e., community membership, as a fundamental model for regular behavior, and thus flag potential anomalies deviating from this pattern. In fact, community membership act as the building blocks of a null model to identify the regular interaction patterns. The structural information is included in the model through latent variables for community membership and anomaly parameter. The algorithm aims at inferring these latent parameters and then output the labels identifying anomalies on the network edges.

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Section: Discussionmentioning

confidence: 86%

Anomaly detection and community detection in networks

Safdari¹,

Bacco²

2022

Preprint

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“…This choice may be more natural in scenarios where individuals form ties on a case-by-case basis rather than predominantly via one of the two mechanisms explored here. This could potentially account for a further mechanism for edge formation, as reciprocity [29][30][31]. Similarly, when node attributes are available along with the network dataset, it would be compelling to adapt the model to suitably incorporate this extra information using insights from previous works [32].…”

Section: Discussionmentioning

confidence: 99%

The interplay between ranking and communities in networks

Iacovissi,

De Bacco

2021

Preprint

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Community detection and hierarchy extraction are usually thought of as separate inference tasks on networks. Considering only one of the two when studying real-world data can be an oversimplification. In this work, we present a generative model based on an interplay between community and hierarchical structures. It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed. The algorithmic implementation is efficient, as it exploits the sparsity of network datasets. We demonstrate our method on synthetic and real-world data and compare performance with two standard approaches for community detection and ranking extraction. We find that the algorithm accurately retrieves each node's preference in different scenarios and we show that it can distinguish small subsets of nodes that behave differently than the majority. As a consequence, the model can recognise whether a network has an overall preferred interaction mechanism. This is relevant in situations where there is no clear "a priori" information about what structure explains the observed network datasets well. Our model allows practitioners to learn this automatically from the data.

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“…Our model specifies conditional probabilities, and thus relies on pseudo-likelihood estimation for inferring the parameters. A fruitful avenue for future research is to improve this approximation by characterizing a full joint distribution of a pair of ties (Contisciani et al, 2021). Doing this may potentially solve the problem of identifying a θ m for samples with high mutuality and, most importantly, increase the accuracy of estimating posterior distributions for Y .…”

Section: Discussionmentioning

confidence: 99%

“…For example, by allowing the distributions of the data to vary from tie to tie, mixture models of networks have been shown to accommodate surveys where respondents have various degrees of trustworthiness (Butts, 2003), some of the data is missing (Peixoto, 2018(Peixoto, , 2019 and more (see . Further, important social phenomena driving tie formation, such as triadic closure, reciprocity and group structure, can be integrated through specification of priors on the network (such as with stochastic block models; Peixoto, 2018Peixoto, , 2021 or latent space models (Butts, 2003)), or by suitably incorporating them into a likelihood distribution (Safdari et al, 2021a,b;Contisciani et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data

Bacco¹,

Contisciani²,

Cardoso-Silva³

et al. 2021

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Social network data are often constructed by incorporating reports from multiple individuals. However, it is not obvious how to reconcile discordant responses from individuals. There may be particular risks with multiply-reported data if people's responses reflect normative expectations-such as an expectation of balanced, reciprocal relationships. Here, we propose a probabilistic model that incorporates ties reported by multiple individuals to estimate the unobserved network structure. In addition to estimating a parameter for each reporter that is related to their tendency of over-or under-reporting relationships, the model explicitly incorporates a term for "mutuality," the tendency to report ties in both directions involving the same alter. Our model's algorithmic implementation is based on variational inference, which makes it efficient and scalable to large systems. We apply our model to data from 75 Indian villages collected with a name-generator design, and a Nicaraguan community collected with a roster-based design. We observe strong evidence of "mutuality" in both datasets, and find that this value varies by relationship type. Consequently, our model estimates networks with reciprocity values that are substantially different than those resulting from standard deterministic aggregation approaches, demonstrating the need to consider such issues when gathering, constructing, and analysing survey-based network data.

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Community detection and reciprocity in networks by jointly modeling pairs of edges

Cited by 4 publications

References 23 publications

Anomaly detection and community detection in networks

Anomaly detection and community detection in networks

The interplay between ranking and communities in networks

Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data

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