Directed clustering in weighted networks: A new perspective

Clemente, Gian Paolo; Grassi, Rosanna

doi:10.1016/j.chaos.2017.12.007

Cited by 85 publications

(56 citation statements)

References 39 publications

(113 reference statements)

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“…where c in = [c in i ] i∈V and c out = [c out i ] i∈V are vectors with entries c in i and c out i , respectively, that represent the in and out weighted local clustering coefficients of node i, while c all is the vector of local clustering coefficient for graph D (see [11]).…”

Section: The High Order Clustering Coefficientmentioning

confidence: 99%

“…Since the graph is directed, we can refer to different kind of geodesics (depending on whether the direction of the arcs is considered or not). We first compute the vector c all of coefficients as provided in [11]. We remind that each coefficient c all i includes all kind of triangles which a node belongs to.…”

Section: Simulated Examplementioning

confidence: 99%

“…Such a measure has been developed in all the cases of weighted, unweighted, directed and undirected networks (see e.g. [1], [11], [16], [29], [33] and [34]). The extension of this community measure to the overall network is obtained by simply taking the average of all the clustering coefficients of the nodes.…”

Section: Introductionmentioning

confidence: 99%

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Systemic risk assessment through high order clustering coefficient

2020

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In this article we propose a novel measure of systemic risk in the context of financial networks.To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order l of a node i introduced in [10]. Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks.

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Section: The High Order Clustering Coefficientmentioning

confidence: 99%

Section: Simulated Examplementioning

confidence: 99%

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Systemic risk assessment through high order clustering coefficient

2020

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“…For instance, money transfers between clients and sellers are best modeled by weighted bipartite directed stream graphs. The concepts we discussed then have to be extended even further, like what has already been done for graphs for instance with the directed strength s + (v) = u∈N + (v) ω(u, v) [39], the directed weighted clustering coefficient [14,19], or for the study of weighted bipartite graphs [22].…”

Section: Resultsmentioning

confidence: 99%

Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks

Latapy

Magnien²,

Viard³

2019

Computational Social Sciences

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We recently introduced a formalism for the modeling of temporal networks, that we call stream graphs. It emphasizes the streaming nature of data and allows rigorous definitions of many important concepts generalizing classical graphs. This includes in particular size, density, clique, neighborhood, degree, clustering coefficient, and transitivity. In this contribution, we show that, like graphs, stream graphs may be extended to cope with bipartite structures, with node and link weights, or with link directions. We review the main bipartite, weighted or directed graph concepts proposed in the literature, we generalize them to the cases of bipartite, weighted, or directed stream graphs, and we show that obtained concepts are consistent with graph and stream graph ones. This provides a formal ground for an accurate modeling of the many temporal networks that have one or several of these features.

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“…It is worth noting the alternatives presented inOnnela et al (2005) as well as the extended versions of the clustering coefficient inFagiolo (2007);Clemente and Grassi (2018).…”

mentioning

confidence: 99%

The interconnectedness of the economic content in the speeches of the US Presidents

Cinelli¹,

Ficcadenti²,

Riccioni³

2019

Ann Oper Res

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The speeches stated by influential politicians can have a decisive impact on the future of a country. In particular, the economic content of such speeches affects the economy of countries and their financial markets. For this reason, we examine a novel dataset containing the economic content of 951 speeches stated by 45 US Presidents from George Washington (April 1789) to Donald Trump (February 2017). In doing so, we use an economic glossary carried out by means of text mining techniques. The goal of our study is to examine the structure of significant interconnections within a network obtained from the economic content of presidential speeches. In such a network, nodes are represented by talks and links by values of cosine similarity, the latter computed using the occurrences of the economic terms in the speeches. The resulting network displays a peculiar structure made up of a core (i.e. a set of highly central and densely connected nodes) and a periphery (i.e. a set of non-central and sparsely connected nodes). The presence of different economic dictionaries employed by the Presidents characterize the core-periphery structure. The Presidents' talks belonging to the network's core share the usage of generic (non-technical) economic locutions like "interest" or "trade". While the use of more technical and less frequent terms characterizes the periphery (e.g. "yield"). Furthermore, the speeches close in time share a common economic dictionary. These results together with the economics glossary usages during the US periods of boom and crisis provide unique insights on the economic content relationships among Presidents' speeches.

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Directed clustering in weighted networks: A new perspective

Cited by 85 publications

References 39 publications

Systemic risk assessment through high order clustering coefficient

Systemic risk assessment through high order clustering coefficient

Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks

The interconnectedness of the economic content in the speeches of the US Presidents

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