The Later Papers of Sir Aubrey Lewis. Introduction by M. Shepherd. (Pp. 245; £10.00.) Oxford University Press: London. 1979.

Newman, Charles

doi:10.1017/s003329170003974x

Cited by 172 publications

(332 citation statements)

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“…The weight of each edge might be proportional to the magnitude of the exposure between two institutions, while edge directionality may allow us to determine who is the creditor and who is the lender. Network theory allows one to statistically characterize the structure of such graphs and taxonomize them according to their similarity or dissimilarity features (Caldarelli, 2007;Newman, 2010;Jackson, 2010).…”

Section: Introductionmentioning

confidence: 99%

Systemic Risk, Contagion, and Financial Networks: A Survey

Chinazzi¹,

Fagiolo

2013

SSRN Journal

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The recent crisis has highlighted the crucial role that existing linkages among banks and financial institutions plays in channeling and amplifying shocks hitting the system. The structure and evolution of such web of linkages can be fruitfully characterized using concepts borrowed from the theory of (complex) networks. This paper critically surveys recent theoretical work that exploits this concept to explain the sources of contagion and systemic risk in financial markets. We taxonomize existing contributions according to the impact of network connectivity, bank heterogeneity, existing uncertainty in financial markets, portfolio composition of the banks. We end with a discussion of the most important challenges faced by theoretical network-based models of systemic risk. These include a better understanding of the causal links between network structure and the likelihood of systemic risk and increasingly using the empirical knowledge about real-world financial-network structures to calibrate theoretical models.

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Section: Introductionmentioning

confidence: 99%

Systemic Risk, Contagion, and Financial Networks: A Survey

Chinazzi¹,

Fagiolo

2013

SSRN Journal

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“…The two key factors in (2) are the noise model and the localized attribute, and we can make independent assumptions on these two. For example, noise can follow Gaussian or Bernoulli distribution and the localization level can be described by small cut costs or cliques [2].…”

Section: Problem Formulationmentioning

confidence: 99%

“…Massive amounts of data being generated from various sources including social networks, citation, biological, and physical infrastructure have spurred the emerging area of analyzing data supported on graphs [1], [2] giving rise to a variety of scientific and engineering studies; for example, selecting representative training data to improve semi-supervised learning with graphs [3]; detecting communities in communication or social networks [4]; ranking the most important websites on the Internet [5]; and detecting anomalies in sensor networks [6].…”

Section: Introductionmentioning

confidence: 99%

Detecting Localized Categorical Attributes on Graphs

Chen

Yang

Zong

et al. 2017

IEEE Trans. Signal Process.

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Abstract-Do users from Carnegie Mellon University form social communities on Facebook? Do signal processing researchers from tightly collaborate with each other? Do Chinese restaurants in Manhattan cluster together? These seemingly different problems share a common structure: an attribute that may be localized on a graph. In other words, nodes activated by an attribute form a subgraph that can be easily separated from other nodes. In this paper, we thus focus on the task of detecting localized attributes on a graph. We are particularly interested in categorical attributes such as attributes in online social networks, ratings in recommender systems and viruses in cyber-physical systems because they are widely used in numerous data mining applications. To solve the task, we formulate a statistical hypothesis testing problem to decide whether a given attribute is localized or not. We propose two statistics: graph wavelet statistic and graph scan statistic, both of which are provably effective in detecting localized attributes. We validate the robustness of the proposed statistics on both simulated data and two real-world applications: high air-pollution detection and keyword ranking in a co-authorship network collected from IEEE Xplore. Experimental results show that the proposed graph wavelet statistic and graph scan statistic are effective and efficient.

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“…It is worth noting that network data, which can be represented by an adjacency matrix, extensively exist in different scientific disciplines, which include but are not limited to anthropology, economics, education, marketing, psychology, physics and sociology. For a good summary, we refer to Holland andLeinhardt (1981), Wasserman andFaust (1994), Knoke and Yang (2008) and Newman (2010). As a consequence, related problems are becoming increasingly popular and important.…”

Section: Introductionmentioning

confidence: 99%

A Popularity Scaled Latent Space Model for Large-Scale Directed Social Network

Chang¹,

Huang²,

Wang³

2019

STAT SINICA

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Large-scale directed social network data often involve degree heterogeneity, reciprocity, and transitivity properties. A sensible network generating model should take these features into consideration. To this end, we propose a popularity scaled latent space model for the large-scale directed network structure formulation. It assumes for each node a position in a hypothetically assumed latent space. Then, the nodes close (far away) to each other should have larger (less) probability to be connected. As a consequence, the reciprocity and transitivity properties can be analytically derived. In addition to that, we assume for each node a popularity parameter. Those nodes with larger (smaller) popularity are more (less) likely to be followed by other nodes. By assuming different distributions for popularity parameters, different types of degree heterogeneity can be modeled. Furthermore, based on the proposed model, a comprehensive probabilistic index is constructed for link prediction. Its finite sample performance is demonstrated by extensive simulation studies and a Sina Weibo (a Twitter-type social network in China) dataset. The performances are competitive.

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The Later Papers of Sir Aubrey Lewis. Introduction by M. Shepherd. (Pp. 245; £10.00.) Oxford University Press: London. 1979.

Cited by 172 publications

References 0 publications

Systemic Risk, Contagion, and Financial Networks: A Survey

Systemic Risk, Contagion, and Financial Networks: A Survey

Detecting Localized Categorical Attributes on Graphs

A Popularity Scaled Latent Space Model for Large-Scale Directed Social Network

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