Criticality assessment of urban interdependent lifeline systems using a biased PageRank algorithm and a multilayer weighted directed network model

2018 ◽  
Vol 22 ◽  
pp. 100-112 ◽  
Author(s):  
Chen Zhao ◽  
Nan Li ◽  
Dongping Fang
Keyword(s):  
Network Model ◽  
Directed Network ◽  
Pagerank Algorithm ◽  
Criticality Assessment ◽  
Lifeline Systems

scholarly journals How many causal pathways must symptoms form before we call them a borderline? A hierarchical network model of borderline personality disorder

2018 ◽  
Author(s):  
Zsolt Unoka ◽  
Mara J. Richman ◽  
Dániel Czégel
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Network Model ◽  
Emotion Dysregulation ◽  
Borderline Personality ◽  
Directed Network ◽  
Causal Relationships ◽  
Hierarchical Network ◽  
Cross Sectional ◽  
Causal Pathways

Borderline personality disorder (BPD) is characterized by impulsivity, emotion dysregulation, disturbed relationships, and identity disturbances. Despite the known variable co-occurrence of BPD symptoms, the possible causal relationships are not well understood. We addressed this by creating a hierarchical network model of BPD, which identifies the most likely acyclic causal pathways that are driving BPD development. Cross-sectional data was obtained from the Structured Clinical Interview-II (SCID-II), and possible causal relationships between symptoms were identified from conditional independence relations. The symptoms’ hierarchy values, assessing their role in causal pathways, was determined by a random walk-based algorithm. By analyzing the directed network of BPD symptoms, it was found that symptoms in initial stages of causal pathways were abandonment, physical fights, impulsivity, suicidal threats, identity disturbances, and affective instability. Based on the assessed role symptoms play in causal pathways of BPD development, specific symptoms can be targeted during early diagnosis and clinical assessment.

The Micro-Blogging Network Leading Group Recognition Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 164-169
Author(s):  
Li Li Dong ◽  
Ke Yang ◽  
Xiang Zhang
Keyword(s):  
Network Model ◽  
Information Dissemination ◽  
Recognition Algorithm ◽  
Personalized Pagerank ◽  
Network Information ◽  
Pagerank Algorithm ◽  
User Influence ◽  
Transmission Of Information ◽  
Network Transmission ◽  
Ranking Order

Based on the analysis and research of micro-blogging network transmission of information, the transmission of information model is constructed. By studying the network model, a small group of core users of the network information dissemination play a guiding role. To solve the problem that the research of micro-blogging user influence ranking can only ranking order given its influence, but not determine which user play a guiding role in transmission of information, LeadersRank algorithm based on the idea of personalized PageRank algorithm is proposed, and the algorithm is applied to the real micro-blogging data to identify the leading group, the experimental results prove the feasibility and effectiveness of the algorithm.

scholarly journals Scaling and crossover functions for the conductance in the directed network model of edge states

1997 ◽  
Vol 55 (16) ◽  
pp. 10593-10601 ◽  
Author(s):  
Ilya A. Gruzberg ◽  
N. Read ◽  
Subir Sachdev
Keyword(s):  
Network Model ◽  
Directed Network ◽  
Edge States

scholarly journals Statistical Inference in a Directed Network Model With Covariates

2018 ◽  
Vol 114 (526) ◽  
pp. 857-868 ◽  
Author(s):  
Ting Yan ◽  
Binyan Jiang ◽  
Stephen E. Fienberg ◽  
Chenlei Leng
Keyword(s):  
Statistical Inference ◽  
Network Model ◽  
Directed Network

scholarly journals High-Order Degree and Combined Degree in Complex Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shudong Wang ◽  
Xinzeng Wang ◽  
Qifang Song ◽  
Yuanyuan Zhang
Keyword(s):  
High Order ◽  
Directed Network ◽  
Damping Factor ◽  
Eigenvector Centrality ◽  
Pagerank Algorithm ◽  
Undirected Network ◽  
Node Importance ◽  
Centrality Metrics ◽  
Special Cases ◽  
Order Degree

We define several novel centrality metrics: the high-order degree and combined degree of undirected network, the high-order out-degree and in-degree and combined out out-degree and in-degree of directed network. Those are the measurement of node importance with respect to the number of the node neighbors. We also explore those centrality metrics in the context of several best-known networks. We prove that both the degree centrality and eigenvector centrality are the special cases of the high-order degree of undirected network, and both the in-degree and PageRank algorithm without damping factor are the special cases of the high-order in-degree of directed network. Finally, we also discuss the significance of high-order out-degree of directed network. Our centrality metrics work better in distinguishing nodes than degree and reduce the computation load compared with either eigenvector centrality or PageRank algorithm.

A normalized score-based weighted PageRank algorithm on ranking prediction of basketball games

2021 ◽  
pp. 2150302
Author(s):  
Yang Chen ◽  
Yepeng Qiu ◽  
Wei Ren
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
Search Algorithm ◽  
Point Of View ◽  
Prediction Methods ◽  
Weighted Network ◽  
Sports Teams ◽  
Pagerank Algorithm ◽  
Chinese University ◽  
Different Types

Ranking of sports teams has always been significant to sponsors, coaches, as well as audiences. Prevailing prediction methods investigate probabilities by taking into account of different kinds of attributes (e.g. field goals, fields goal attempts) in order to establish a detail-based mechanism for analyzing the capability among competing teams. The different types of activation and inhibition actions between athletes provide a considerable challenge in the framework of network analysis. Moreover, these attributes interactions might add up the substantial redundancy to network frame as well. This paper proposes a weighted PageRank algorithm based on the normalized basketball match scores from a macroscopic point of view. Taking Chinese Basketball Association and Chinese University Basketball Association as examples, the developed approach takes into account the win/lose nature of interactions between each pair of competing teams in the framework of PageRank network. We also evolve a weighted network model for the network matrix which highlights the capability difference of teams whose PageRank probabilities are most sensitive with respect to the scores of the two competing teams. The chance of championship of teams can be better demonstrated by the PageRank probabilities. The results show that our method achieves more precise predicting result than that of original PageRank algorithm and Hypertext-Induced Topic Search algorithm.

Sign in / Sign up

Export Citation Format

Share Document