scholarly journals Top influencers can be identified universally by combining classical centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Doina Bucur
Keyword(s):  
Information Flow ◽  
Real World ◽  
Eigenvector Centrality ◽  
Average Precision ◽  
Statistical Classifiers ◽  
The Core ◽  
Node Centrality ◽  
Statistical Boundary ◽  
Central Regions ◽  
Peripheral Regions

AbstractInformation flow, opinion, and epidemics spread over structured networks. When using node centrality indicators to predict which nodes will be among the top influencers or superspreaders, no single centrality is a consistently good ranker across networks. We show that statistical classifiers using two or more centralities are instead consistently predictive over many diverse, static real-world topologies. Certain pairs of centralities cooperate particularly well in drawing the statistical boundary between the superspreaders and the rest: a local centrality measuring the size of a node’s neighbourhood gains from the addition of a global centrality such as the eigenvector centrality, closeness, or the core number. Intuitively, this is because a local centrality may rank highly nodes which are located in locally dense, but globally peripheral regions of the network. The additional global centrality indicator guides the prediction towards more central regions. The superspreaders usually jointly maximise the values of both centralities. As a result of the interplay between centrality indicators, training classifiers with seven classical indicators leads to a nearly maximum average precision function (0.995) across the networks in this study.

scholarly journals GSN: A Graph-Structured Network for Multi-Party Dialogues

2019 ◽  
Author(s):  
Wenpeng Hu ◽  
Zhangming Chan ◽  
Bing Liu ◽  
Dongyan Zhao ◽  
Jinwen Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Information Flow ◽  
Real World ◽  
Experimental Results ◽  
Neural Models ◽  
The Core ◽  
Dialogue Modeling ◽  
Special Case

Existing neural models for dialogue response generation assume that utterances are sequentially organized. However, many real-world dialogues involve multiple interlocutors (i.e., multi-party dialogues), where the assumption does not hold as utterances from different interlocutors can occur ``in parallel.'' This paper generalizes existing sequence-based models to a Graph-Structured neural Network (GSN) for dialogue modeling. The core of GSN is a graph-based encoder that can model the information flow along the graph-structured dialogues (two-party sequential dialogues are a special case). Experimental results show that GSN significantly outperforms existing sequence-based models.

The globalization of social sciences? Evidence from a quantitative analysis of 30 years of production, collaboration and citations in the social sciences (1980–2009)

2013 ◽  
Vol 62 (5) ◽  
pp. 626-646 ◽  
Author(s):  
Sébastien Mosbah-Natanson ◽  
Yves Gingras
Keyword(s):  
Social Sciences ◽  
Quantitative Analysis ◽  
Social Science Research ◽  
Science Research ◽  
Social Scientists ◽  
The Core ◽  
The Social ◽  
International Collaborations ◽  
Central Regions ◽  
Peripheral Regions

This article addresses the issue of internationalization of social sciences by studying the evolution of production (of academic articles), collaboration and citations patterns among main world regions over the period 1980–2009 using the SSCI. The results confirm the centre–periphery model and indicate that the centrality of the two major regions that are North America and Europe is largely unchallenged, Europe having become more important and despite the growing development of Asian social sciences. The authors’ quantitative approach shows that the growing production in the social sciences but also the rise of international collaborations between regions have not led to a more homogeneous circulation of the knowledge produced by different regions, or to a substantial increase in the visibility of the contributions produced by peripheral regions. Social scientists from peripheral regions, while producing more papers in the core journals compiled by the SSCI, have a stronger tendency to cite journals from the two central regions, thus losing at least partially their more locally embedded references, and to collaborate more with western social scientists. In other words, the dynamic of internationalization of social science research may also lead to a phagocytosis of the periphery into the two major centers, which brings with it the danger of losing interest in the local objects specific to those peripheral regions.

Centrality-Based Connected Dominating Sets for Complex Network Graphs

2014 ◽  
Vol 6 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Complex Network ◽  
Real World ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Dominating Sets ◽  
Eigenvector Centrality ◽  
Connected Dominating Sets ◽  
The Core ◽  
Centrality Metrics ◽  
Betweeness Centrality

The author proposes the use of centrality-metrics to determine connected dominating sets (CDS) for complex network graphs. The author hypothesizes that nodes that are highly ranked by any of these four well-known centrality metrics (such as the degree centrality, eigenvector centrality, betweeness centrality and closeness centrality) are likely to be located in the core of the network and could be good candidates to be part of the CDS of the network. Moreover, the author aims for a minimum-sized CDS (fewer number of nodes forming the CDS and the core edges connecting the CDS nodes) while using these centrality metrics. The author discusses our approach/algorithm to determine each of these four centrality metrics and run them on six real-world network graphs (ranging from 34 to 332 nodes) representing various domains. The author observes the betweeness centrality-based CDS to be of the smallest size in five of the six networks and the closeness centrality-based CDS to be of the smallest size in the smallest of the six networks and incur the largest size for the remaining networks.

scholarly journals Sublinear domination and core–periphery networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marios Papachristou
Keyword(s):  
Network Model ◽  
Power Law ◽  
Real World ◽  
Random Network ◽  
Small World ◽  
The Core ◽  
Entire Network

AbstractIn this paper we devise a generative random network model with core–periphery properties whose core nodes act as sublinear dominators, that is, if the network has n nodes, the core has size o(n) and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.

scholarly journals Betweenness centrality for temporal multiplexes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Silvia Zaoli ◽  
Piero Mazzarisi ◽  
Fabrizio Lillo
Keyword(s):  
Information Flow ◽  
Real World ◽  
Betweenness Centrality ◽  
Temporal Structure ◽  
Shortest Paths ◽  
Single Layer ◽  
Distance Metric ◽  
Definition Of

AbstractBetweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.

Lexical Arguments and Information Types in Malay Oral Narratives

2015 ◽  
Vol 7 (1) ◽  
pp. 80-97
Author(s):  
Siaw-Fong Chung
Keyword(s):  
Information Flow ◽  
The Other ◽  
Discourse Structure ◽  
Oral Narratives ◽  
Clear Cut ◽  
The Core ◽  
New Information ◽  
Intonation Units ◽  
Locative Expressions ◽  
Transitive Verbs

The analysis in this paper was based on five Malay narratives of the “frog story”. In these narratives, the types of lexical arguments and their relations with information flow and topic continuity were analyzed. It was found that most narrators used one lexical argument in telling the frog story (e.g., sarang itu jatuh “the nest fell”). About 60% of the verbs in the narratives contained one lexical argument only. Some transitive verbs that usually require the presence of both lexical arguments were used with one lexical argument only when produced in speech (e.g., dia mencari ø di merata tempat “he searched (for) ø everywhere”). Objects were sometimes omitted, as their meanings could be predicted from previous context. Despite the omission of objects, transitive constructions still prevailed in the stories. The most frequently occurring lexical arguments were objects (O) (37%), followed by intransitive subjects (S) (29%) and transitive subjects (A) (27%). In addition, our results showed that new information in Malay was usually allocated to the core argument of the object and to locative expressions, indicating that most of the new information appeared at the end of a clause. On the other hand, topic continuity was held between the subjects in two continuous intonation units. This clear-cut division of discourse functions in the heads and tails of constructions was consistently found in the five pieces of narration. This observation not only showed how ideas could be continued in Malay oral narratives, but also contributes to the study of discourse structure in Malay.

Pattern of Bone Generation after Irradiation in Vascularized Tissue Engineered Constructs

2017 ◽  
Vol 34 (02) ◽  
pp. 130-137
Author(s):  
Ibrahim Fathi ◽  
Ahmed Eltawila ◽  
Ahmad Elsherif ◽  
Yasser Elkerm ◽  
Leila Harhaus ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Bone Formation ◽  
Head And Neck ◽  
Neck Cancer ◽  
Autogenous Bone ◽  
Heterotopic Bone ◽  
External Beam Irradiation ◽  
Central Regions ◽  
Axial Vascularization ◽  
Peripheral Regions

Background Regenerative medicine modalities provide promising alternatives to conventional reconstruction techniques but are still deficient after malignant tumor excision or irradiation due to defective vascularization. Methods We investigated the pattern of bone formation in axially vascularized tissue engineering constructs (AVTECs) after irradiation in a study that mimics the clinical scenario after head and neck cancer. Heterotopic bone generation was induced in a subcutaneously implanted AVTEC in the thigh of six male New Zealand rabbits. The tissue construct was made up of Nanobone (Artoss GmbH; Rostock, Germany) granules mixed with autogenous bone marrow and 80 μL of bone morphogenic protein-2 at a concentration of 1.5 μg/μL. An arteriovenous loop was created microsurgically between the saphenous vessels and implanted in the core of the construct to induce axial vascularization. The constructs were subjected to external beam irradiation on postoperative day 20 with a single dose of 15 Gy. The constructs were removed 20 days after irradiation and subjected to histological and immunohistochemical analysis for vascularization, bone formation, apoptosis, and cellular proliferation. Results The vascularized constructs showed homogenous vascularization and bone formation both in their central and peripheral regions. Although vascularity, proliferation, and apoptosis were similar between central and peripheral regions of the constructs, significantly more bone was formed in the central regions of the constructs. Conclusion The study shows for the first time the pattern of bone formation in AVTECs after irradiation using doses comparable to those applied after head and neck cancer. Axial vascularization probably enhances the osteoinductive properties in the central regions of AVTECs after irradiation.

scholarly journals Real Time Blind Source Separation in Reverberant Environments

2021 ◽  
Author(s):  
◽  
Timothy Sherry
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real World Data ◽  
The Core ◽  
Reverberant Environments ◽  
Ica Algorithm ◽  
Stationary Sources ◽  
Convolutive Blind Source Separation

<p>An online convolutive blind source separation solution has been developed for use in reverberant environments with stationary sources. Results are presented for simulation and real world data. The system achieves a separation SINR of 16.8 dB when operating on a two source mixture, with a total acoustic delay was 270 ms. This is on par with, and in many respects outperforms various published algorithms [1],[2]. A number of instantaneous blind source separation algorithms have been developed, including a block wise and recursive ICA algorithm, and a clustering based algorithm, able to obtain up to 110 dB SIR performance. The system has been realised in both Matlab and C, and is modular, allowing for easy update of the ICA algorithm that is the core of the unmixing process.</p>

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2019 ◽  
pp. 1183-1198
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

The authors present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. They consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC), and betweenness centrality (BWC). They define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. The authors observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. They observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2018 ◽  
pp. 6507-6521
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

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