A matter of time - intrinsic or extrinsic - for diffusion in evolving complex networks

Albano, Alice; Guillaume, Jean‐Loup; Heymann, Sébastien; Grand, Bénédicte Le

doi:10.1145/2492517.2492634

Cited by 7 publications

(9 citation statements)

References 17 publications

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“…a disease outbreak) starts. As an intermediate step to creating simple graphs, Holme [103] uses weighted graph obtained by, for ever contact between a node pair, summing an exponential weight i e −ti/τ (1) (where t i is the time between the beginning of the spreading and the i'th contact, and τ is a parameter that should match the time scale of the dynamic process).…”

Section: Weighted Graphsmentioning

confidence: 99%

“…Refs. [1,2,257]). Another measure for a similar purpose-to monitor the growth of the network-is to measure the fraction of nodes or links present at half of the sampling time, or half of the total number of contacts [109].…”

Section: Time Aspects Of Network Evolutionmentioning

confidence: 99%

“…Miritello, Moro and Lara [196] map a spreading problem to percolation. Indeed, the SI model [247,24,243,75,1] is equivalent to invasion percolation.…”

Section: Percolation Error Tolerance and Attack Vulnerabilitymentioning

confidence: 99%

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Modern temporal network theory: a colloquium

2015

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Abstract. The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

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Section: Weighted Graphsmentioning

confidence: 99%

Section: Time Aspects Of Network Evolutionmentioning

confidence: 99%

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Modern temporal network theory: a colloquium

2015

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“…Whereas extrinsic time is broadly used without notice, previous works have shown that the choice between extrinsic and intrinsic time has a very significant impact both on the measurement of statistical properties of temporal networks and on observation of diffusion processes [32], [33]. Previous results on social networks suggest that intrinsic time is better at characterizing the endogenous dynamics of the network, because extrinsic time is more likely to capture exogenous patterns like day-night activity of users.…”

Section: Different Notions Of Timementioning

confidence: 99%

On the use of intrinsic time scale for dynamic community detection and visualization in social networks

Albano

Guillaume

Grand

2014

2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)

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International audienceThe analysis of social networks is a challenging research area, in particular because of their dynamic features. In this paper, we study such evolving graphs through the evolution of their community structure. More specifically, we build on existing approaches for the identification of stable communities over time. This paper presents two contributions. We first propose a new way to compute such stable communities, using a different time scale, called intrinsic time. This intrinsic time is related to the dynamics of the graph (e.g., in terms of link appearance or disappearance) and independent from traditional (extrinsic) time units, like the second. We then show how visualization both at intrinsic and extrinsic time scales can help validating and interpreting the obtained communities. Our results are illustrated on a social network made of contacts among the participants of the 2006 edition of the Infocom conference

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“…Ref. [24]) and focus on the structure of greedy walks, i.e. their order of visited nodes, step by step.…”

Section: Introductionmentioning

confidence: 99%

Exploring temporal networks with greedy walks

Saramäki

Holme

2015

Eur. Phys. J. B

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Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timings of node and link activations. In this paper, we set to explore the latter by using greedy walks as probes of temporal network structure. Given a temporal network (a sequence of contacts), greedy walks proceed from node to node by always following the first available contact. Because of this, their structure is particularly sensitive to temporal-topological patterns involving repeated contacts between sets of nodes. This becomes evident in their small coverage per step as compared to a temporal reference model -in empirical temporal networks, greedy walks often get stuck within small sets of nodes because of correlated contact patterns. While this may also happen in static networks that have pronounced community structure, the use of the temporal reference model takes the underlying static network structure out of the equation and indicates that there is a purely temporal reason for the observations. Further analysis of the structure of greedy walks indicates that burst trains, sequences of repeated contacts between node pairs, are the dominant factor. However, there are larger patterns too, as shown with non-backtracking greedy walks. We proceed further to study the entropy rates of greedy walks, and show that the sequences of visited nodes are more structured and predictable in original data as compared to temporally uncorrelated references. Taken together, these results indicate a richness of correlated temporal-topological patterns in temporal networks.

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A matter of time - intrinsic or extrinsic - for diffusion in evolving complex networks

Cited by 7 publications

References 17 publications

Modern temporal network theory: a colloquium

Modern temporal network theory: a colloquium

On the use of intrinsic time scale for dynamic community detection and visualization in social networks

Exploring temporal networks with greedy walks

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