Analysis of information propagation in academic social networks

2016 ◽  
Author(s):  
S Selva Kumar ◽  
K Sailaja Kumar ◽  
N. Kayarvizhy
Keyword(s):  
Social Networks ◽  
Information Propagation ◽  
Academic Social Networks

Research output availability on academic social networks: implications for stakeholders in academic publishing

2017 ◽  
Vol 27 (2) ◽  
pp. 125-133 ◽  
Author(s):  
Mikael Laakso ◽  
Juho Lindman ◽  
Cenyu Shen ◽  
Linus Nyman ◽  
Bo-Christer Björk
Keyword(s):  
Social Networks ◽  
Research Output ◽  
Academic Publishing ◽  
Academic Social Networks

Community detection in social networks based on information propagation and user engagement

2021 ◽  
pp. 2150119
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu ◽  
Xin Guo
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Method ◽  
User Engagement ◽  
Connection Strength ◽  
Information Propagation ◽  
Edge Strength ◽  
Strength Based ◽  
Network Propagation ◽  
Overlapping Community

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales.

scholarly journals Academic Social Networks: How the web is changing our way to make and communicate researches

2015 ◽  
Vol 7 (2) ◽  
pp. 3-14 ◽  
Author(s):  
Giovanni Bonaiuti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Sites ◽  
Rapid Evolution ◽  
Scientific Field ◽  
Academic Social Network ◽  
Academic Social Networks ◽  
Insight Into ◽  
The Web ◽  
Natural Component

Abstract Networking is not only essential for success in academia, but it should also be seen as a natural component of the scholarly profession. Research is typically not a purely individualistic enterprise. Academic social network sites give researchers the ability to publicise their research outputs and connect with each other. This work aims to investigate the use done by Italian scholars of 11/D2 scientific field. The picture presented shows a realistic insight into the Italian situation, although since the phenomenon is in rapid evolution results are not stable and generalizable.

scholarly journals A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks

2021 ◽  
Vol 33 (1) ◽  
pp. 47-70
Author(s):  
Santhoshkumar Srinivasan ◽  
Dhinesh Babu L. D.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Propagation Model ◽  
Information Propagation ◽  
Control Approach ◽  
Rumor Propagation ◽  
Immunization Strategies ◽  
New Information ◽  
The Individual ◽  
Rumor Control

Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to reduce the damage it causes to society. Educating the individual participants of OSNs is one of the effective ways to control the rumor faster. To educate people in OSNs, this paper proposes a defensive rumor control approach that spreads anti-rumors by the inspiration from the immunization strategies of social insects. In this approach, a new information propagation model is defined to study the defensive nature of true information against rumors. Then, an anti-rumor propagation method with a set of influential spreaders is employed to defend against the rumor. The proposed approach is compared with the existing rumor containment approaches and the results indicate that the proposed approach works well in controlling the rumors.

Coupling entropy of co-processing model on social networks

2015 ◽  
Vol 29 (25) ◽  
pp. 1550149
Author(s):  
Zhanli Zhang
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Structural Characteristics ◽  
Time Lag ◽  
Optimal Routing ◽  
Information Propagation ◽  
Time Processing ◽  
Scale Free ◽  
Routing Strategy ◽  
Processing Ability

Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

Academic social networks and collaboration patterns

2019 ◽  
Vol 38 (2) ◽  
pp. 293-307
Author(s):  
Po-Yen Chen
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Research Collaboration ◽  
Open Data ◽  
Data Sets ◽  
Content Type ◽  
Open Government Data ◽  
Collaboration Patterns ◽  
Academic Social Networks ◽  
Government Data

Purpose This study attempts to use a new source of data collection from open government data sets to identify potential academic social networks (ASNs) and defines their collaboration patterns. The purpose of this paper is to propose a direction that may advance our current understanding on how or why ASNs are formed or motivated and influence their research collaboration. Design/methodology/approach This study first reviews the open data sets in Taiwan, which is ranked as the first state in Global Open Data Index published by Open Knowledge Foundation to select the data sets that expose the government’s R&D activities. Then, based on the theory review of research collaboration, potential ASNs in those data sets are identified and are further generalized as various collaboration patterns. A research collaboration framework is used to present these patterns. Findings Project-based social networks, learning-based social networks and institution-based social networks are identified and linked to various collaboration patterns. Their collaboration mechanisms, e.g., team composition, motivation, relationship, measurement, and benefit-cost, are also discussed and compared. Originality/value In traditional, ASNs have usually been known as co-authorship networks or co-inventorship networks due to the limitation of data collection. This study first identifies some ASNs that may be formed before co-authorship networks or co-inventorship networks are formally built-up, and may influence the outcomes of research collaborations. These information allow researchers to deeply dive into the structure of ASNs and resolve collaboration mechanisms.

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