Community identity in a temporal network: A taxonomy proposal

Pereira, Luis Ramada; Lopes, Rui J.; Louçã, Jorge

doi:10.1016/j.ecocom.2020.100904

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…LargeNetVis leverages three taxonomies focused on different aspects of community categorization -the community's structural, temporal, and evolutionary behaviors -to guide the network exploration. The categories considered by these taxonomies have also been analyzed by previous works [23,40,43,49,50,56,72] and represent the core of our approach.…”

Section: Network Community Taxonomiesmentioning

confidence: 99%

“…The final aspect that we consider is the network community evolution, which is essential to understand the dynamics and lifecycle of these groups of nodes, with events such as birth and growth. We consider the community taxonomy proposed by Pereira et al [50], which includes six categories of evolutionary events: Birth, Death, Grow, Contract, Split and Merge. The first appearance of a community is classified as Birth, while disappearance is considered as Death.…”

Section: Network Community Taxonomiesmentioning

confidence: 99%

“…This work was supported by grants #2020/10049-0, #2020/07200-9, and #2016/17078-0 from São Paulo Research Foundation (FAPESP), grant #E-26/201.424/2021 from Carlos Chagas Filho Foundation for Research Support of Rio de Janeiro State (FAPERJ), CNPq, CAPES, and by the School of Applied Mathematics at Fundac ¸ão Getulio Vargas (FGV). The authors thank Luis R. Pereira for providing an optimized version of his source-code [50].…”

Section: Acknowledgmentsmentioning

confidence: 99%

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LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Linhares¹,

Ponciano

Pedro³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. LargeNetVis is a web-based visual analytics system designed to support the visual exploration of temporal networks with up to a few thousand nodes and timestamps. The system is composed of four linked visual components. While the Taxonomy Matrix (A) and Global View (B) enable global-level analysis, the node-link diagram (C) and the Temporal Activity Map (D) enable structural and temporal local-level analysis, respectively. The system also provides a panel with the network, community, or node numerical information (Community detail (E)). A tooltip with extra information is shown when a community is hovered over in Global View (F).

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Section: Network Community Taxonomiesmentioning

confidence: 99%

Section: Network Community Taxonomiesmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

See 1 more Smart Citation

LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Linhares¹,

Ponciano

Pedro³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…Several studies have been carried out to extend the model and propose modified models of temporal networks, such as the activity-security-trust-driven model [27], the activity-driven model with memory [28], the competition-driven model [29], etc. Some scholars have discussed the topology structures of the temporal network and focused on key nodes [30,31], temporal motifs [32][33][34], community structure [35,36], and other topology structures, such as degree, correlation, path length, clustering coefficient, and entropy [37][38][39][40]. Other studies have explored spreading dynamics in the temporal network, such as information spreading [41,42], knowledge diffusion [43,44], and disease transmission [45,46].…”

Section: Introductionmentioning

confidence: 99%

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Liu

Chen

et al. 2023

Systems

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With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.

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Describing group evolution in temporal data using multi-faceted events

Failla,

Cazabet,

Rossetti

et al. 2024

Mach Learn

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Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.

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Community identity in a temporal network: A taxonomy proposal

Cited by 4 publications

References 26 publications

LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Describing group evolution in temporal data using multi-faceted events

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