scholarly journals Minimising Entropy Changes in Dynamic Network Evolution

2017 ◽  
pp. 255-265 ◽  
Author(s):  
Jianjia Wang ◽  
Richard C. Wilson ◽  
Edwin R. Hancock
Keyword(s):  
Dynamic Network ◽  
Network Evolution ◽  
Entropy Changes

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

scholarly journals Modelling dynamic network evolution as a Pitman-Yor process

2019 ◽  
Vol 1 (3) ◽  
pp. 293-306
Author(s):  
Francesco Sanna Passino ◽  
◽  
Nicholas A. Heard
Keyword(s):  
Dynamic Network ◽  
Network Evolution

scholarly journals Link Prediction with Spatial and Temporal Consistency in Dynamic Networks

2017 ◽  
Author(s):  
Wenchao Yu ◽  
Wei Cheng ◽  
Charu C Aggarwal ◽  
Haifeng Chen ◽  
Wei Wang
Keyword(s):  
Link Prediction ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Evolution ◽  
Generic Model ◽  
Temporal Consistency ◽  
Temporal Network ◽  
Close Proximity ◽  
Two Factors ◽  
Random Node

Dynamic networks are ubiquitous. Link prediction in dynamic networks has attracted tremendous research interests. Many models have been developed to predict links that may emerge in the immediate future from the past evolution of the networks. There are two key factors: 1) a node is more likely to form a link in the near future with another node within its close proximity, rather than with a random node; 2) a dynamic network usually evolves smoothly. Existing approaches seldom unify these two factors to strive for the spatial and temporal consistency in a dynamic network. To address this limitation, in this paper, we propose a link prediction model with spatial and temporal consistency (LIST), to predict links in a sequence of networks over time. LIST characterizes the network dynamics as a function of time, which integrates the spatial topology of network at each timestamp and the temporal network evolution. Comparing to existing approaches, LIST has two advantages: 1) LIST uses a generic model to express the network structure as a function of time, which makes it also suitable for a wide variety of temporal network analysis problems beyond the focus of this paper; 2) by retaining the spatial and temporal consistency, LIST yields better prediction performance. Extensive experiments on four real datasets demonstrate the effectiveness of the LIST model.

scholarly journals Evolution of the plandemic communication network among serial participants on Twitter

2021 ◽  
pp. 146144482110509
Author(s):  
Yu Xu ◽  
Yao Sun ◽  
Loni Hagen ◽  
Mihir Patel ◽  
Mary Falling
Keyword(s):  
Communication Network ◽  
Social Order ◽  
Dynamic Network ◽  
Anger Expression ◽  
Analytical Framework ◽  
Structural Features ◽  
Network Evolution ◽  
Conspiracy Theory ◽  
Political Preference ◽  
New Social Order

The coronavirus pandemic has been accompanied by the spread of misinformation on social media. The Plandemic conspiracy theory holds that the pandemic outbreak was planned to create a new social order. This study examines the evolution of this popular conspiracy theory from a dynamic network perspective. Guided by the analytical framework of network evolution, the current study explores drivers of tie changes in the Plandemic communication network among serial participants over a 4-month period. Results show that tie changes are explained by degree-based and closure-based structural features (i.e. tendencies toward transitive closure and shared popularity and tendencies against in-degree activity and transitive reciprocated triplet) and nodal attributes (i.e. bot probability and political preference). However, a participant’s level of anger expression does not predict the evolution of the observed network.

DYNAMIC EVOLUTION MODEL BASED ON SOCIAL NETWORK SERVICES

2013 ◽  
Vol 24 (11) ◽  
pp. 1350081 ◽  
Author(s):  
XI XIONG ◽  
ZHI-JIAN GOU ◽  
SHI-BIN ZHANG ◽  
WEN ZHAO
Keyword(s):  
Social Network ◽  
Confidence Limit ◽  
Dynamic Network ◽  
Network Evolution ◽  
Evolution Model ◽  
Network Services ◽  
Dynamic Evolution ◽  
Dynamic Changes ◽  
High Betweenness ◽  
High Connectivity

Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

EgoVis: A Visual Analysis System for Social Networks Based on Egocentric Research

2019 ◽  
Vol 29 (01n02) ◽  
pp. 1930003
Author(s):  
Qiang Lu ◽  
Jing Huang ◽  
Yifan Ge ◽  
Dajiu Wen ◽  
Bin Chen ◽  
...  
Keyword(s):  
Social Network ◽  
Visual Analysis ◽  
Dynamic Network ◽  
Citation Network ◽  
Network Evolution ◽  
Individual Characteristics ◽  
Three Dimensions ◽  
Evolutionary Pattern ◽  
Analysis System ◽  
The Individual

The development of crowd intelligence makes the structure of social network more complex and changeable. Research on social network should be more in-depth and focus on the changes of structure. Ego-network, which represents the relationship between specific individual and the related people, is a hot issue among the research of dynamic social network. The evolution of ego-network is highly dynamic and pluralistic, it is hard to capture its evolutionary pattern over time. To help users analyze the individual characteristics and hidden patterns in multivariate ego-network, we present EgoVis, an interactive visual analysis system for exploring and analyzing complex structural relationships in dynamic network. Based on the task requirements of network evolution analysis, we propose a task taxonomy which is suitable for ego-network research and analysis, design novel visual fonts, and analyze the evolution of dynamic ego-network relations from the three dimensions: overview, subgroup, and detail-ego. Finally, the validity and practicability of EgoVis are verified on DBLP citation network dataset.

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