Link persistence and conditional distances in multiplex networks

Papadopoulos, Fragkiskos; Kleineberg, Kaj-Kolja

doi:10.1103/physreve.99.012322

Cited by 13 publications

(14 citation statements)

References 60 publications

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“…We also note that memory in the dynamic-S 1 is induced only via the nodes' latent variables (κ, θ). Extensions to the model with link persistence, where connections/disconnections can also be copied from the previous to the next snapshot [49,50], would allow additional control over the rate of dynamics, i.e., on how fast the topology changes from snapshot to snapshot. The dynamic-S 1 or extensions of it may apply to other types of timevarying networks, such as the ones considered in [51,52], and constitute the basis of maximum likelihood estimation methods that infer the node coordinates and their evolution in the latent spaces of real systems [53].…”

Section: Discussionmentioning

confidence: 99%

Latent geometry and dynamics of proximity networks

Papadopoulos

Flores

2019

Phys. Rev. E

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Proximity networks are time-varying graphs representing the closeness among humans moving in a physical space. Their properties have been extensively studied in the past decade as they critically affect the behavior of spreading phenomena and the performance of routing algorithms. Yet, the mechanisms responsible for their observed characteristics remain elusive. Here, we show that many of the observed properties of proximity networks emerge naturally and simultaneously in a simple latent space network model, called dynamic-S 1 . The dynamic-S 1 does not model node mobility directly, but captures the connectivity in each snapshot-each snapshot in the model is a realization of the S 1 model of traditional complex networks, which is isomorphic to hyperbolic geometric graphs. By forgoing the motion component the model facilitates mathematical analysis, allowing us to prove the contact, inter-contact and weight distributions. We show that these distributions are power laws in the thermodynamic limit with exponents lying within the ranges observed in real systems. Interestingly, we find that network temperature plays a central role in network dynamics, dictating the exponents of these distributions, the time-aggregated agent degrees, and the formation of unique and recurrent components. Further, we show that paradigmatic epidemic and rumor spreading processes perform similarly in real and modeled networks. The dynamic-S 1 or extensions of it may apply to other types of time-varying networks and constitute the basis of maximum likelihood estimation methods that infer the node coordinates and their evolution in the latent spaces of real systems.

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Section: Discussionmentioning

confidence: 99%

Latent geometry and dynamics of proximity networks

Papadopoulos

Flores

2019

Phys. Rev. E

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“…Trans-layer link prediction is about finding missing links in one layer using a similarity measure on another layer, and its effectiveness has been evaluated in contrast to binary link predictor which is based on edge overlap. Also, it is shown that geometric correlations are not enough to explain the high edge overlap in real multiplex networks and a link persistence factor can both improve the reproduction of edge overlap and improve performance of trans-layer link prediction [29].…”

Section: Related Workmentioning

confidence: 99%

Link prediction in real-world multiplex networks via layer reconstruction method

et al. 2020

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Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

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“…That is, every user must be labelled with the same pseudonym throughout the sequence of snapshots where it appears. Consistent annotation is of paramount importance for a number of analysis tasks such as community evolution analysis [6], link prediction [15], link persistence analysis [25], among others, that require to track users along the sequence of releases. The data owner anonymises every snapshot exactly once.…”

Section: Overviewmentioning

confidence: 99%

“…As we discussed in Sect. 3.2, the data owner must assign the same pseudonym to each user throughout the subsequence of snapshots where it appears, to allow for analysis tasks such as community evolution analysis [6], link prediction [15], link persistence analysis [25], etc. Since the data owner cannot distinguish between legitimate users (including victims) and sybil accounts, she will assign time-persistent pseudonyms to all of them.…”

Section: Temporal Consistency Constraintsmentioning

confidence: 99%

“…Social networks are inherently dynamic. Moreover, analysts require datasets containing dynamic social graphs in order to conduct numerous tasks such as community evolution analysis [6], link prediction [15] and link persistence analysis [25], among others. Despite the need for properly anonymised dynamic social graphs, the overwhelming majority of studies on graph anonymisation have focused on the scenario of a social graph being released only once.…”

Section: Introductionmentioning

confidence: 99%

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Active Re-identification Attacks on Periodically Released Dynamic Social Graphs

Chen,

Këpuska,

Mauw

et al. 2019

Preprint

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Active re-identification attacks pose a serious threat to privacy-preserving social graph publication. Active attackers create fake accounts to build structural patterns in social graphs which can be used to re-identify legitimate users on published anonymised graphs, even without additional background knowledge. So far, this type of attacks has only been studied in the scenario where the inherently dynamic social graph is published once. In this paper, we present the first active re-identification attack in the more realistic scenario where a dynamic social graph is periodically published. The new attack leverages tempo-structural patterns for strengthening the adversary. Through a comprehensive set of experiments on real-life and synthetic dynamic social graphs, we show that our new attack substantially outperforms the most effective static active attack in the literature by increasing the success probability of re-identification by more than two times and efficiency by almost 10 times. Moreover, unlike the static attack, our new attack is able to remain at the same level of effectiveness and efficiency as the publication process advances. We conduct a study on the factors that may thwart our new attack, which can help design graph anonymising methods with a better balance between privacy and utility.

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Link persistence and conditional distances in multiplex networks

Cited by 13 publications

References 60 publications

Latent geometry and dynamics of proximity networks

Latent geometry and dynamics of proximity networks

Link prediction in real-world multiplex networks via layer reconstruction method

Active Re-identification Attacks on Periodically Released Dynamic Social Graphs

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