A model of indirect contagion based on a news similarity network

2021 ◽  
Vol 9 (5) ◽  
Author(s):  
Daniel O Cajueiro ◽  
Saulo B Bastos ◽  
Camila C Pereira ◽  
Roberto F S Andrade
Keyword(s):  
Similarity Network ◽  
Stationary Probabilities

Abstract Our objective is to model indirect contagion among companies. We use pieces of news about companies to measure the similarities between them. We assume that two companies are similar if we may associate a story about one company with the story about another company and vice-versa. First, after statistically eliminating arbitrary similarities between companies, we introduce a network based on the news similarities. Second, we evaluate a vector of stationary probabilities associated with the process of contagion that takes place in the network and we use these pieces of information to define the notion of centrality. Third, we use this vector of stationary probabilities to build an associated graph that reveals the most important paths of information contagion. Finally, we build a model of indirect contagion and evaluate the size of the information avalanches that take place in the similarity network.

On a Markovian queue with weakly correlated interarrival times

1981 ◽  
Vol 18 (01) ◽  
pp. 190-203 ◽  
Author(s):  
Guy Latouche
Keyword(s):  
Time Distribution ◽  
Queueing System ◽  
Interarrival Time ◽  
Probability Vector ◽  
Common Value ◽  
Markovian Queue ◽  
The Stability ◽  
Number Of Customers ◽  
Stationary Probabilities ◽  
Stationary Probability Vector

A queueing system with exponential service and correlated arrivals is analysed. Each interarrival time is exponentially distributed. The parameter of the interarrival time distribution depends on the parameter for the preceding arrival, according to a Markov chain. The parameters of the interarrival time distributions are chosen to be equal to a common value plus a factor ofε, where ε is a small number. Successive arrivals are then weakly correlated. The stability condition is found and it is shown that the system has a stationary probability vector of matrix-geometric form. Furthermore, it is shown that the stationary probabilities for the number of customers in the system, are analytic functions ofε, for sufficiently smallε, and depend more on the variability in the interarrival time distribution, than on the correlations.

Stochastic ordering of random processes with an imbedded point process

1991 ◽  
Vol 28 (3) ◽  
pp. 553-567 ◽  
Author(s):  
François Baccelli
Keyword(s):  
Point Process ◽  
Continuous Time ◽  
Random Sequence ◽  
Stochastic Ordering ◽  
Random Processes ◽  
Time Process ◽  
Continuous Time Process ◽  
Partial Orderings ◽  
Stationary Probabilities

We introduce multivariate partial orderings related with the Palm and time-stationary probabilities of a point process. Using these orderings, we give conditions for the monotonicity of a random sequence, with respect to some integral stochastic ordering, to be inherited with a continuous time process in which this sequence is imbedded. This type of inheritance is also discussed for the property of association.

Prediction of drug–target interaction by label propagation with mutual interaction information derived from heterogeneous network

2016 ◽  
Vol 12 (2) ◽  
pp. 520-531 ◽  
Author(s):  
Xiao-Ying Yan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang
Keyword(s):  
Heterogeneous Network ◽  
Drug Target ◽  
Label Propagation ◽  
Mutual Interaction ◽  
Potential Drug Target ◽  
Potential Drug ◽  
Similarity Network ◽  
Target Interaction ◽  
Interaction Information

By implementing label propagation on drug/target similarity network with mutual interaction information derived from drug–target heterogeneous network, LPMIHN algorithm identifies potential drug–target interactions.

scholarly journals ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing

2020 ◽  
Vol 36 (8) ◽  
pp. 2602-2604 ◽  
Author(s):  
Evangelos Karatzas ◽  
Juan Eiros Zamora ◽  
Emmanouil Athanasiadis ◽  
Dimitris Dellis ◽  
Zoe Cournia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Perfect Match ◽  
Web Server ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
Structural Similarity ◽  
Substructure Search ◽  
Similarity Network ◽  
Compound Libraries ◽  
Network Analysis Metrics

Abstract Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.

scholarly journals Component Evolution in Random Intersection Graphs

10.37236/935 ◽  
2007 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Behrisch
Keyword(s):  
Real World ◽  
Linear Order ◽  
Graph Model ◽  
Vertex Degree ◽  
Intersection Graph ◽  
Giant Component ◽  
Logarithmic Order ◽  
Intersection Graphs ◽  
Similarity Network ◽  
Random Intersection Graph

We study the evolution of the order of the largest component in the random intersection graph model which reflects some clustering properties of real–world networks. We show that for appropriate choice of the parameters random intersection graphs differ from $G_{n,p}$ in that neither the so-called giant component, appearing when the expected vertex degree gets larger than one, has linear order nor is the second largest of logarithmic order. We also describe a test of our result on a protein similarity network.

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