Information flows and crashes in dynamic social networks

2021 ◽  
Author(s):  
Phillip J. Monin ◽  
Richard Bookstaber
Keyword(s):  
Social Networks ◽  
Information Flows ◽  
Dynamic Social Networks

scholarly journals Influenza epidemic model using dynamic social networks of individuals with cognition maps

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 101030
Author(s):  
Leonardo López ◽  
Maximiliano Fernández ◽  
Leonardo Giovanini
Keyword(s):  
Social Networks ◽  
Epidemic Model ◽  
Influenza Epidemic ◽  
Dynamic Social Networks

Evolutionary Community Discovery in Dynamic Social Networks via Resistance Distance

2020 ◽  
pp. 114536
Author(s):  
Weimin Li ◽  
Heng Zhu ◽  
Shaohua Li ◽  
Hao Wang ◽  
Hongning Dai ◽  
...  
Keyword(s):  
Social Networks ◽  
Resistance Distance ◽  
Community Discovery ◽  
Dynamic Social Networks

Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks

2021 ◽  
Vol 15 (4) ◽  
pp. 1-22
Author(s):  
Huan Wang ◽  
Chunming Qiao ◽  
Xuan Guo ◽  
Lei Fang ◽  
Ying Sha ◽  
...  
Keyword(s):  
Social Networks ◽  
Structural Change ◽  
Structural Changes ◽  
Structural Information ◽  
Similarity Index ◽  
Dynamic Social Network ◽  
Dynamic Social Networks ◽  
Micro Level ◽  
Anomaly Analysis ◽  
Similarity Method

Recently, dynamic social network research has attracted a great amount of attention, especially in the area of anomaly analysis that analyzes the anomalous change in the evolution of dynamic social networks. However, most of the current research focused on anomaly analysis of the macro representation of dynamic social networks and failed to analyze the nodes that have anomalous structural changes at a micro level. To identify and evaluate anomalous structural change-based nodes in generalized dynamic social networks that only have limited structural information, this research considers undirected and unweighted graphs and develops a multiple-neighbor superposition similarity method ( ), which mainly consists of a multiple-neighbor range algorithm ( ) and a superposition similarity fluctuation algorithm ( ). introduces observation nodes, characterizes the structural similarities of nodes within multiple-neighbor ranges, and proposes a new multiple-neighbor similarity index on the basis of extensional similarity indices. Subsequently, maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, based on and , not only identifies anomalous structural change-based nodes by detecting the anomalous structural changes of nodes but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that can accurately identify anomalous structural change-based nodes and evaluate their anomalous degrees well.

SIMiner: A Stigmergy-Based Model for Mining Influential Nodes in Dynamic Social Networks

2019 ◽  
Vol 5 (2) ◽  
pp. 223-237 ◽  
Author(s):  
Weihua Li ◽  
Quan Bai ◽  
Minjie Zhang
Keyword(s):  
Social Networks ◽  
Dynamic Social Networks ◽  
Influential Nodes
Sign in / Sign up

Export Citation Format

Share Document