A method combining community detection and change point detection to identify stable communities in link streams at multiple temporal scales.
Stream Graphs and Link Streams for the Modeling of Interactions over Time
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Graph theory provides a language for studying the structure of relations, and it is often used to study interactions over time too. However, it poorly captures the both temporal and structural nature of interactions, that calls for a dedicated formalism. In this paper, we generalize graph concepts in order to cope with both aspects in a consistent way. We start with elementary concepts like density, clusters, or paths, and derive from them more advanced concepts like cliques, degrees, clustering coefficients, or connected components. We obtain a language to directly deal with interactions over time, similar to the language provided by graphs to deal with relations. This formalism is self-consistent: usual relations between different concepts are preserved. It is also consistent with graph theory: graph concepts are special cases of the ones we introduce. This makes it easy to generalize higher-level objects such as quotient graphs, line graphs, k-cores, and centralities. This paper also considers discrete versus continuous time assumptions, instantaneous links, and extensions to more complex cases.
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fields
cs.SI 3years
2019 3verdicts
UNVERDICTED 3roles
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background 1representative citing papers
This review chapter outlines key challenges in discovering dynamic communities in temporal networks and recent propositions to address them.
A thesis compiling the author's works on bursty dynamics, temporal network methods, and data-driven modeling of socioeconomic patterns and social contagion.
citing papers explorer
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Detecting Stable Communities in Link Streams at Multiple Temporal Scales
A method combining community detection and change point detection to identify stable communities in link streams at multiple temporal scales.
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Challenges in Community Discovery on Temporal Networks
This review chapter outlines key challenges in discovering dynamic communities in temporal networks and recent propositions to address them.
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Computational Human Dynamics
A thesis compiling the author's works on bursty dynamics, temporal network methods, and data-driven modeling of socioeconomic patterns and social contagion.