The Structure of Complex Networks

Estrada, Ernesto

doi:10.1093/acprof:oso/9780199591756.001.0001

Cited by 531 publications

(515 citation statements)

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“…This brings to life a scenario that social scientists have for many years been using as a theoretical tool to develop concepts and measures. In particular, given only a network interaction structure, perhaps describing social acquaintanceship, it has proved extremely useful to imagine that information flows along the links and thereby to identify important actors [10,27]. In this setting, most centrality measures are defined through, or can be motivated from, the idea of studying random walks along the edges [26], or deterministically counting geodesics, paths, trails or walks [7].…”

Section: Building the Active Node Subnetwork Sequencementioning

confidence: 99%

“…Using the fact that (A p ) ij records the number of distinct walks 1 of length p from node i to node j [10], we see that the (i, j) element of (I −αA) −1 counts the total number of walks of all possible length, with walks of length p downweighted by α p . The idea of attaching less importance to longer walks is intuitively reasonable, and Katz [18] also points out that α may be intepreted probabilistically, as the chance that a message successfully traverses an edge.…”

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confidence: 99%

“…We note that these two quantities are also widely used as centrality measures in their own right [10,27]. In recent years, several authors have pointed out that concepts such as geodesics, paths and walks can be extended to the case of a time-ordered sequence of networks [5,15,[19][20][21]25].…”

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confidence: 99%

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Dynamic Targeting in an Online Social Medium

Laflin

Mantzaris

Ainley

et al. 2012

Lecture Notes in Computer Science

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Abstract. Online human interactions take place within a dynamic hierarchy, where social influence is determined by qualities such as status, eloquence, trustworthiness, authority and persuasiveness. In this work, we consider topic-based Twitter interaction networks, and address the task of identifying influential players. Our motivation is the strong desire of many commerical entities to increase their social media presence by engaging positively with pivotal bloggers and tweeters. After discussing some of the issues involved in extracting useful interaction data from a Twitter feed, we define the concept of an active node subnetwork sequence. This provides a time-dependent, topic-based, summary of relevant Twitter activity. For these types of transient interactions, it has been argued that the flow of information, and hence the influence of a node, is highly dependent on the timing of the links. Some nodes with relatively small bandwidth may turn out to be key players because of their prescience and their ability to instigate follow-on network activity. To simulate a commercial application, we build an active node subnetwork sequence based on key words in the area of travel and holidays. We then compare a range of network centrality measures, including a recently proposed version that accounts for the arrow of time, with respect to their ability to rank important nodes in this dynamic setting. The centrality rankings use only connectivity information (who Tweeted whom, when), but if we post-process the results by examining account details, we find that the time-respecting, dynamic, approach, which looks at the follow-on flow of information, is less likely to be 'misled' by accounts that appear to generate large numbers of automatic Tweets with the aim of pushing out web links. We then benchmark these algorithmically derived rankings against independent feedback from five social media experts who judge Twitter accounts as part of their professional duties. We find that the dynamic centrality measures add value to the expert view, and indeed can be hard to distinguish from an expert in terms of who they place in the top ten. We also highlight areas where the algorithmic approach can be refined and improved.

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Section: Building the Active Node Subnetwork Sequencementioning

confidence: 99%

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confidence: 99%

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Dynamic Targeting in an Online Social Medium

Laflin

Mantzaris

Ainley

et al. 2012

Lecture Notes in Computer Science

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“…Many biological systems can be described in terms of their interaction patterns (Buchanan, 2010) and thus are naturally modelled using the tools of network science (Newman, 2010;Estrada, 2011). Such models typically take the form of a simple or directed graph, in which the vertices represent the different components of the system under investigation, and a link is formed between vertex pairs if they interact in some way.…”

Section: Introductionmentioning

confidence: 99%

Complexity and robustness in hypernetwork models of metabolism

Pearcy

Chuzhanova

Crofts

2016

Journal of Theoretical Biology

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Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking oversimplification of the the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable environments displayed increased levels of robustness and topological complexity.

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“…Many biological systems can be described using the techniques of network science 1,2 , which provides a powerful set of tools for analysing the underlying connectivity structures that naturally arise within all living organisms 3,4 . At the cellular level, networks emerge via interacting proteins, and other macro-molecules, resulting in various biochemical nets, such as gene regulatory networks 5,6 , protein-protein interaction networks 7 and protein residue networks 8,9 .…”

Section: Introductionmentioning

confidence: 99%

Network motif frequency vectors reveal evolving metabolic network organisation

2015

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At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

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The Structure of Complex Networks

Cited by 531 publications

References 0 publications

Dynamic Targeting in an Online Social Medium

Dynamic Targeting in an Online Social Medium

Complexity and robustness in hypernetwork models of metabolism

Network motif frequency vectors reveal evolving metabolic network organisation

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