Mapping Change in Large Networks

By Martin Rosvall, Carl T. Bergstrom. PLoS ONE, 2008

657 citations recorded by the source corpus.

Change is a fundamental ingredient of interaction patterns in biology, technology, the economy, and science itself: Interactions within and between organisms change; transportation patterns by air, land, and sea all change; the global financial flow changes; and the frontiers of scientific research change. Networks and clustering methods have become important tools to comprehend instances of these large-scale structures, but without methods to distinguish between real trends and noisy data, these approaches are not useful for studying how networks change. Only if we can assign significance to the partitioning of single networks can we distinguish meaningful structural changes from random fluctuations. Here we show that bootstrap resampling accompanied by significance clustering provides a solution to this problem. To connect changing structures with the changing function of networks, we highlight and summarize the significant structural changes with alluvial diagrams and realize de Solla Price's vision of mapping change in science: studying the citation pattern between about 7000 scientific journals over the past decade, we find that neuroscience has transformed from an interdisciplinary specialty to a mature and stand-alone discipline.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3514b60bdde2bbde93f80f193fbc41893b289262

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Citations in the corpus

Tradition and Innovation in Scientists’ Research Strategies (2013, methodology): “Most quantitative analyses of large-scale scientific behavior use the document as the fundamental unit of analysis (12, 13) and leverage citation data to identify innovations (14) and outline the structure of scientific fields (15).”
Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems (2010, background): “Figures 2A–C provide thumbnail illustrations of the hierarchical structure of the journal citation network of science [20], the global air traffic network [21], and the human disease network [22].”
Detecting the dynamics of urban structure through spatial network analysis (2014, methodology): “The changing communities in terms of volume of flows, number of communities, and their sequences were previously shown in Figure 1, using the concept of the alluvial diagram due to (Rosvall et al. 2010) based on data taken from the different community clusters at the three points in time 2010, 2011,”
Science mapping software tools: Review, analysis, and cooperative study among tools (2011, methodology): “…techniques have been used: Cluster string (Small, 2006; Small & Upham, 2009; Upham & Small, 2010), rolling clustering (Kandylas et al., 2010), alluvial diagrams (Rosvall & Bergstrom, 2010), ThemeRiver visualization (Havre, Hetzler, Whitney, & Nowell, 2002), and thematic areas (Cobo et al., 2011).”
Temporal Networks (2011, background): “[22]), or they have divided the time into segments and studied the structure of contacts accumulated over those segments [118].”
A review of theory and practice in scientometrics (2015, background): “Bollen et al.. (2009) developed maps based on clickstream data; Rosvall & Bergstrom (2010) proposed to use alluvial maps for showing the dynamics of science.”
An Efficient Framework for Generating Storyline Visualizations from Streaming Data (2015, neutral): “In summary, this experiment demonstrates our framework’s excellent capability to create legible storyline visualizations.”
Memory in network flows and its effects on spreading dynamics and community detection (2013, methodology): “ng modules. Therefore, we limit the search space here and only allow each memory node to be assigned to a single module. In this way, the efﬁcient (27) search algorithm for hard partitions in Infomap (28,29) can be used with only small modiﬁcations. Instead of applying the search algorithm on the st”
ggalluvial: Layered Grammar for Alluvial Plots (2020, background): “These in turn anticipated “alluvial diagrams”, proposed to visualize changes in case memberships across successive cross-sections (Rosvall & Bergstrom, 2010).”
SciMAT: A new science mapping analysis software tool (2012, methodology): “…or multidimensional scaling, clustering algorithms (Chen et al., 2010; Chen & Redner, 2010; Coulter et al., 1998; Kandylas, Upham, & Ungar, 2010; Rosvall & Bergstrom, 2010; Small and Sweeney, 1985), and Pathfinder networks (PFNETs) (Quirin, Cordón, Santamaría, Vargas-Quesada, & Moya-Anegón,…”
Community detection in networks: A user guide (2016, background): “…network is supposed to belong to (bootstrapping), and comparing the clusterings found in those configurations, to check how frequently subsets of vertices are clustered together in different samples, which is an index of the robustness (significance) of their clusters (Rosvall and Bergstrom, 2010).”
Stable Community Cores in Complex Networks (2012, background): “For example, in [17] it is proposed to change the weight of links using a Poisson distribution whose parameter is the average weight of links in the original graph.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “Reproduced with permission from Rosvall and Bergstrom (2010). A”
Community Discovery in Dynamic Networks (2017, neutral): “A similar idea is proposed in (Rosvall and Bergstrom, 2010): alluvial diagrams are used to represent community evolution: each node is represented as a continuous line, whose vertical position is determined by its community aﬃliation.”
Modern temporal network theory: a colloquium (2015, neutral): “Rosvall and Bergstrom [253], Bazzi et al.”