The structured backbone of temporal social ties

Abstract

In many data sets, crucial information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems, for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections, the "network backbone", is a non-trivial task, and methods put forward until now do not address time-resolved networks, whose availability has strongly increased in recent years. We develop here such a method, by defining an adequate temporal network null model, which calculates the random chance of nodes to be connected at any time after controlling for their activity. This allows us to identify, at any level of statistical significance, pairs of nodes that have more interactions than expected given their activities: These form a backbone of significant ties. We apply our method to empirical temporal networks of socio-economic interest and find that (i) at given level of statistical significance, our method identifies more significant ties than methods considering temporally aggregated networks, and (ii) when a community structure is present, most significant ties are intra-community edges, suggesting that the weights of inter-community edges can be explained by the null model of random interactions. Most importantly, our filtering method can assign a significance to more complex structures such as triads of simultaneous interactions, while methods based on static representations are by construction unable to do so. Strikingly, we uncover that significant triads are not equivalent to triangles composed by three significant edges. Our results hint at new ways to represent temporal networks for use in data-driven models and in anonymity-preserving ways.

arXiv commentMain text: 18 pages, 6 figures. SI: 22 pages, 17 figures

References in corpus (34)

• Finding and evaluating community structure in networks
• Maps of random walks on complex networks reveal community structure
• Identification of influential spreaders in complex networks
• Temporal Networks
• Analysis of weighted networks
• Structure and tie strengths in mobile communication networks
• Extracting the multiscale backbone of complex weighted networks
• High-resolution measurements of face-to-face contact patterns in a primary school
• Dynamics of person-to-person interactions from distributed RFID sensor networks
• High resolution dynamical mapping of social interactions with active RFID
• Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
• Class of correlated random networks with hidden variables
• Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors
• Temporal motifs in time-dependent networks
• Statistically validated networks in bipartite complex systems
• Can co-location be used as a proxy for face-to-face contacts?
• Network structure from rich but noisy data
• A fitness model for the Italian Interbank Money Market
• Social network dynamics of face-to-face interactions
• Circadian pattern and burstiness in mobile phone communication
• An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
• Estimating network structure from unreliable measurements
• Compensating for population sampling in simulations of epidemic spread on temporal contact networks
• A parametric approach to information filtering in complex networks: The Pólya filter
• Statistically validated mobile communication networks: Evolution of motifs in European and Chinese data
• Identifying relationship lending in the interbank market: A network approach
• From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
• Irreducible network backbones: unbiased graph filtering via maximum entropy
• Social dynamics of financial networks
• Fast filtering and animation of large dynamic networks
• Community Structures, Interactions and Dynamics in London's Bicycle Sharing Network
• Comparing spatial networks: A 'one size fits all' efficiency-driven approach
• Revealing latent factors of temporal networks for mesoscale intervention in epidemic spread
• Extracting the multi-timescale activity patterns of online financial markets

Cited by in corpus (15)

• Emergence of polarized ideological opinions in multidimensional topic spaces
• From temporal network data to the dynamics of social relationships
• An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks
• Meta-validation of bipartite network projections
• Building surrogate temporal network data from observed backbones
• Extracting the signed backbone of intrinsically dense weighted networks
• Quantifying agent impacts on contact sequences in social interactions
• Two types of densification scaling in the evolution of temporal networks
• Characterization of interactions' persistence in time-varying networks
• The switching mechanisms of social network densification
• Do you trade with your friends or become friends with your trading partners? A case study in the G1 cryptocurrency
• Dynamic models of gentrification
• Identifying the temporal dynamics of densification and sparsification in human contact networks
• Impact of temporal connectivity patterns on epidemic process
• Identifying maximal sets of significantly interacting nodes in higher-order networks