Introducing Multilayer Stream Graphs and Layer Centralities

Parmentier, Pimprenelle; Viard, Tiphaine; Renoust, Benjamin; Baffier, Jean-François

doi:10.1007/978-3-030-36683-4_55

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Stream graphs can easily extend and generalize static centrality measures as the betweenness [37], and analyze empirically the differences in shortest, fastest, foremost time-respecting paths [35,36]. Recently, augmented stream graphs models have been defining, as in modeling interactions over time with multilayer structure [24], where the focus is on the definition of new layer centrality measures.…”

Section: Dynamics Of Networkmentioning

confidence: 99%

$$\Delta $$-Conformity: multi-scale node assortativity in feature-rich stream graphs

Citraro

Milli

Cazabet

et al. 2022

Int J Data Sci Anal

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Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the time-varying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: $$\Delta $$ Δ -Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes’ connectivity along a period of time $$\Delta $$ Δ starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce $$\Delta $$ Δ -Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.

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Section: Dynamics Of Networkmentioning

confidence: 99%

$$\Delta $$-Conformity: multi-scale node assortativity in feature-rich stream graphs

Citraro

Milli

Cazabet

et al. 2022

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…Stream Graphs can easily extend and generalize static centrality measures as the betweenness [34], and analyze empirically the differences in shortest, fastest, foremost time-respecting paths [32,33]. Recently, augmented stream graphs models have been defining, as in modeling interactions over time with multi-layer structure [22],…”

mentioning

confidence: 99%

Δ-Conformity: Multi-scale Node Assortativity in Feature-rich Stream Graphs

Citraro¹,

Milli²,

Cazabet³

et al. 2021

Preprint

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Heterogeneity is a key aspect of complex networks, often emerging by looking at the distribution of node properties, from the milestone observations on the degree to the recent developments in mixing pattern estimation. Mixing patterns, in particular, refer to nodes' connectivity preferences with respect to an attribute label. Social networks are mostly characterized by assortative/homophilic behaviour, where nodes are more likely to be connected with similar ones. Recently, assortative mixing is increasingly measured in a multi-scale fashion to overcome well-known limitations of classic scores. Such multi-scale strategies can capture heterogeneous behaviors among node homophily, but they ignore an important, often available, addendum in real-world systems: the time when edges are present and the time-varying paths they form accordingly. Hence, temporal homophily is still little understood in complex networks. In this work we aim to cover this gap by introducing the ∆-Conformity measure, a multi-scale, path-aware, node homophily estimator within the new framework of feature-rich stream graphs. A rich experimental section analyzes ∆-Conformity trends over time, spanning the analysis from real-life social interaction networks to a specific case-study about the Bitcoin Transaction Network.

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