weg2vec: Event embedding for temporal networks

Torricelli, Maddalena; Karsai, Márton; Gauvin, Laetitia

doi:10.1038/s41598-020-63221-2

Cited by 32 publications

(23 citation statements)

References 43 publications

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“…For instance, recent proposals include static lossless representations of temporal networks, notably the supra-adjacency representation method [Valdano et al, 2015] and the event-graph [Kivelä et al, 2018], in which nodes and interactions are suitably mapped onto the nodes and links of static networks. These representations have shown to be useful for embedding and prediction tasks [Sato et al, 2019, Torricelli et al, 2020].…”

Section: Discussionmentioning

confidence: 99%

From temporal network data to the dynamics of social relationships

Gelardi

Barrat

Claidière

2021

Preprint

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Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks.

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Section: Discussionmentioning

confidence: 99%

From temporal network data to the dynamics of social relationships

Gelardi

Barrat

Claidière

2021

Preprint

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“…RRM were also used in temporal embedding 30 , inference of structures in communication networks 31 and analysing collective behaviour in social networks 32 .…”

Section: Introductionmentioning

confidence: 99%

Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

Barjašić

Štefančić

Pribičević

et al. 2021

Preprint

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Motivated by the detection of cascades of defaults in economy, we developed a detection framework for an endogenous spreading based on causal motifs we define in this paper. We assume that the change of state of a vertex can be triggered by an endogenous or an exogenous event, that the underlying network is directed and that times when vertices changed their states are available. In addition to the data of company defaults, we also simulate cascades driven by different stochastic processes on different synthetic networks. We show that some of the smallest motifs can robustly detect endogenous spreading events. Finally, we apply the method to the data of defaults of Croatian companies and observe the time window in which an endogenous cascade was likely happening.

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“…RRM were also used in the temporal embedding 38 and the inference of structures in communication networks 39 . For analysing collective behaviour in social networks 40 , the author used two-event motifs without causal ordering and also benchmarked the results to the RRM.…”

Section: Introductionmentioning

confidence: 99%

Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

Barjašić

Štefančić

Pribičević

et al. 2021

Sci Rep

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Motivated by the problem of detection of cascades of defaults in economy, we developed a detection framework for an endogenous spreading based on causal motifs we define in this paper. We assume that the change of state of a vertex can be triggered either by an endogenous (related to the network) or an exogenous (unrelated to the network) event, that the underlying network is directed and that times when vertices changed their states are available. After simulating default cascades driven by different stochastic processes on different synthetic networks, we show that some of the smallest causal motifs can robustly detect endogenous spreading events. Finally, we apply the method to the data of defaults of Croatian companies and observe the time window in which an endogenous cascade was likely happening.

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weg2vec: Event embedding for temporal networks

Cited by 32 publications

References 43 publications

From temporal network data to the dynamics of social relationships

From temporal network data to the dynamics of social relationships

Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

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