Neighbourhood matching creates realistic surrogate temporal networks

Abstract

Temporal networks are essential for modeling and understanding systems whose behavior varies in time, from social interactions to biological systems. Often, however, real-world data are prohibitively expensive to collect or unshareable due to privacy concerns. A promising solution is 'surrogate networks', synthetic graphs with the properties of real-world networks. Until now, the generation of realistic surrogate temporal networks has remained an open problem, due to the difficulty of capturing both the temporal and topological properties of the input network, as well as their correlations, in a scalable model. Here, we propose a novel and simple method for generating surrogate temporal networks. By decomposing graphs into temporal neighborhoods surrounding each node, we can generate new networks using neighborhoods as building blocks. Our model vastly outperforms current methods across multiple examples of temporal networks in terms of both topological and dynamical similarity. We further show that beyond generating realistic interaction patterns, our method is able to capture intrinsic temporal periodicity of temporal networks, all with an execution time lower than competing methods by multiple orders of magnitude.

Modeling framework unifying contact and social networks

“…Parameters corresponding to each hypothesis are then tuned by a genetic algorithm to maximize the similarity between model instances and a given empirical data set. While such similarity can be defined a priori in many ways, we find that using only the ETN vector to quantify it and tune the parameters leads to an improvement for many other observables, indicating that many statistical properties of a social temporal network are related to its ETN motifs [27]. We recall that the ETN vector is given by the list and frequencies of ETN motifs at various levels of aggregation (1 to 10 in our case), which thus encodes several spatiotemporal scales.…”

“…e. Egocentric Temporal Network (ETN) [26,27]: An ETN (see Fig. 6) corresponds to a representation of the diversity of the interaction partners of a given node (the ego, in red in Fig.…”

“…We do not intend to answer the question of which list of observables would fully characterize a social system represented as a temporal network, as this question is not fully answered even for static network representations [25]. However we extend the set of commonly used observables: we consider the distributions of the node activity duration and interduration, and of the duration of newly established edges, as well as structural patterns such as the size of connected components in the instantaneous graph of interactions, and spatio-temporal patterns like Egocentric Temporal Networks (ETN) [26], which have recently been shown to be useful building blocks to decompose a temporal network [27].…”

Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets

“…In the last decades, graphs has been involved in many domains such as: molecular biology [7], face-to-face interactions [8], [9], contact tracing [10] and for social networks [11], [12]. Graph structures are capable of informing powerful modeling in deep learning, due to their non-euclidean domain.…”

Emotion Analysis Using Multilayered Networks for Graphical Representation of Tweets

“…In the last decades, graph analysis has been involved in many domains such as: molecular biology [10], face-to-face interactions [11], [12], for social networks [13], [14], crime [15], [16] and many others [17], [18]. For NLP problems, graphs are very useful for representing text because the syntactic relationship between words in a sentence can naturally form a graph structure [19].…”