scholarly journals Towards Network Perspective of Intra-Organizational Learning: Bridging the Gap between Acquisition and Participation Perspective

10.28945/3124 ◽  
2007 ◽  
Author(s):  
Miha Skerlavaj ◽  
Vlado Dimovski
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Organizational Learning ◽  
Communities Of Practice ◽  
Case Studies ◽  
Dynamic Process ◽  
Real Life ◽  
Scientific Field ◽  
Individual Group ◽  
Multi Level

Organizational learning is a scientific field of growing importance. It has developed from classic and foundational works to the two disparate perspectives today: the acquisition and the participation perspective. The first understands knowledge as a substance, mind as a container, and learning as a transfer of a substance from one mind to another. The second perspective focuses on communities of practice and observes no teaching but rather goal-directed practical learning. We argue that both are incomplete and that there is a need for overarching perspective that would build upon multiple-theoretical and multi-level framework of social network theories. Beside connecting acquisition and participation perspective it addresses organizational learning as a multiplex and dynamic process at individual, group, intra-organizational, as well as relational level of research. This contribution proposes network perspective to intra-organizational learning and develops seven descriptive claims to be tested using real-life case studies of social networks within organizations. Both exploratory and confirmatory social network techniques are to be applied.

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 Who comes to mind? Dynamic construction of social networks

2020 ◽  
Author(s):  
Joseph Bayer ◽  
Neil Anthony Lewis ◽  
Jonathan Stahl
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Dynamic Process ◽  
Social Groups ◽  
Social Resources ◽  
Personal Relationships ◽  
Cognitive Mechanisms ◽  
Relational Structures ◽  
Network Technologies

Much remains unknown about moment-to-moment social-network cognition — that is, who comes to mind as we go about our day-to-day lives. Responding to this void, we describe the real-time construction of cognitive social networks. First, we outline the types of relational structures that comprise momentary networks, distinguishing the roles of personal relationships, social groups, and mental sets. Second, we discuss the cognitive mechanisms that determine which individuals are activated — and which are neglected — through a dynamic process. Looking forward, we contend that these overlooked mechanisms need to be considered in light of emerging network technologies. Finally, we chart the next steps for understanding social-network cognition across real-world contexts, along with the built-in implications for social resources and intergroup disparities.

Social Networks and Terrorism

2011 ◽  
pp. 232-246 ◽  
Author(s):  
David Knoke
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Data Collection ◽  
Case Studies ◽  
Recent Case ◽  
Future Research ◽  
Data Collection And Analysis ◽  
Tools And Methods

This chapter explains how international terror networks, consisting of individuals and organizations spanning countries and continents, form and evolve. It describes tools and methods used by social network analysts to study such networks; their applications by counterterrorist organizations; their limitations and problems in data collection and analysis; and directions for future research. It also discusses a few recent case studies by prominent researchers.

A Social Network Perspective on Knowledge Management

2011 ◽  
pp. 2070-2078 ◽  
Author(s):  
Reed E. Nelson ◽  
HY Sonya Hsu
Keyword(s):  
Social Networks ◽  
Knowledge Management ◽  
Social Network ◽  
Organizational Learning

Social networks—the sets of relations that link individuals and collectives—have implications for the speed and effectiveness with which knowledge is created and disseminated in organizations Both social networks and knowledge management (KM) are complex, multifaceted phenomena that are as yet imperfectly understood. Not unsurprisingly, our understanding of the interface between the two is similarly imperfect and evolving. There are, however, a number of foundational concepts upon which existing thought converges as well as a body of emerging research that offers practical and conceptual guidance for developing the kind of network best suited for managing different kinds of knowledge. In this article, we introduce rudimentary network concepts, briefly recapitulate KM and organizational learning concepts related to networks, and then explore some of the interfaces between social networks and KM.

A Social Network Perspective on Knowledge Management

2011 ◽  
pp. 826-832
Author(s):  
Reed E. Nelson ◽  
H.Y. Sonya Hsu
Keyword(s):  
Social Networks ◽  
Knowledge Management ◽  
Social Network ◽  
Organizational Learning

Social networks—the sets of relations that link individuals and collectives—have implications for the speed and effectiveness with which knowledge is created and disseminated in organizations Both social networks and knowledge management (KM) are complex, multifaceted phenomena that are as yet imperfectly understood. Not unsurprisingly, our understanding of the interface between the two is similarly imperfect and evolving. There are, however, a number of foundational concepts upon which existing thought converges as well as a body of emerging research that offers practical and conceptual guidance for developing the kind of network best suited for managing different kinds of knowledge. In this article, we introduce rudimentary network concepts, briefly recapitulate KM and organizational learning concepts related to networks, and then explore some of the interfaces between social networks and KM.

scholarly journals Understanding attribute and social circle correlation in social networks

2019 ◽  
pp. 1228-1242
Author(s):  
PRANAV NERURKAR ◽  
MADHAV CHANDANE ◽  
SUNIL BHIRUD
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Network ◽  
Real Life ◽  
Coarse Grained ◽  
Unified Framework ◽  
Community Discovery ◽  
Social Circle ◽  
The Social ◽  
Circle Formation

Social circles, groups, lists, etc. are functionalities that allow users of online social network (OSN) platforms to manually organize their social media contacts. However, this facility provided by OSNs has not received appreciation from users due to the tedious nature of the task of organizing the ones that are only contacted periodically. In view of the numerous benefits of this functionality, it may be advantageous to investigate measures that lead to enhancements in its efficacy by allowing for automatic creation of customized groups of users (social circles, groups, lists, etc). The field of study for this purpose, i.e. creating coarse-grained descriptions from data, consists of two families of techniques, community discovery and clustering. These approaches are infeasible for the purpose of automation of social circle creation as they fail on social networks. A reason for this failure could be lack of knowledge of the global structure of the social network or the sparsity that exists in data from social networking websites. As individuals do in real life, OSN clients dependably attempt to broaden their groups of contacts in order to fulfill different social demands. This means that ‘homophily’ would exist among OSN users and prove useful in the task of social circle detection. Based on this intuition, the current inquiry is focused on understanding ‘homophily’ and its role in the process of social circle formation. Extensive experiments are performed on egocentric networks (ego is user, alters are friends) extracted from prominent OSNs like Facebook, Twitter, and Google+. The results of these experiments are used to propose a unified framework: feature extraction for social circles discovery (FESC). FESC detects social circles by jointly modeling ego-net topology and attributes of alters. The performance of FESC is compared with standard benchmark frameworks using metrics like edit distance, modularity, and running time to highlight its efficacy.

Classifying the Influential Individuals in Multi-Layer Social Networks

2019 ◽  
Vol 8 (1) ◽  
pp. 21-32
Author(s):  
Ruchi Mittal ◽  
M.P.S Bhatia
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Real Life ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Centrality Measures ◽  
Learning Techniques ◽  
Modes Of Interaction ◽  
Source Of Information

Nowadays, social media is one of the popular modes of interaction and information diffusion. It is commonly found that the main source of information diffusion is done by some entities and such entities are also called as influencers. An influencer is an entity or individual who has the ability to influence others because of his/her relationship or connection with his/her audience. In this article, we propose a methodology to classify influencers from multi-layer social networks. A multi-layer social network is the same as a single layer social network depict that it includes multiple properties of a node and modeled them into multiple layers. The proposed methodology is a fusion of machine learning techniques (SVM, neural networks and so on) with centrality measures. We demonstrate the proposed algorithm on some real-life networks to validate the effectiveness of the approach in multi-layer systems.

A novel genetic algorithm-based improvement model for online communities and trust networks

2021 ◽  
Vol 40 (1) ◽  
pp. 1597-1608
Author(s):  
Ilker Bekmezci ◽  
Murat Ermis ◽  
Egemen Berki Cimen
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Online Communities ◽  
Real Life ◽  
Statistical Properties ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.

scholarly journals SOCIO-SEMANTIC FRAMEWORKS

2013 ◽  
Vol 16 (04n05) ◽  
pp. 1350013 ◽  
Author(s):  
CAMILLE ROTH
Keyword(s):  
Social Network ◽  
Case Studies ◽  
State Of The Art ◽  
Knowledge Exchange ◽  
Software Developers ◽  
Independent Manner ◽  
Network Characteristics ◽  
Exchange Information ◽  
Multi Level ◽  
Technical Systems

Socio-technical systems involve agents who create and process knowledge, exchange information and create ties between ideas in a distributed and networked manner: webloggers, communities of scientists, software developers and wiki contributors are, among others, examples of such networks. The state-of-the-art in this regard focuses on two main issues which are generally addressed in an independent manner: the description of content dynamics and the study of social network characteristics and evolution. This paper relies on recent endeavors to merge both types of dynamics into co-evolutionary, multi-level modeling frameworks, where social and semantic aspects are being jointly appraised. Case studies featuring socio-semantic graphs, socio-semantic hypergraphs and socio-semantic lattices are notably discussed.

scholarly journals Identification of Enterprise Social Network (ESN) Group Archetypes in ESN Analytics

2020 ◽  
Vol 24 ◽  
Author(s):  
Kai Riemer ◽  
Laurence Lock Lee ◽  
Cai Kjaer ◽  
Annika Haeffner
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Communities Of Practice ◽  
Activity Patterns ◽  
Project Teams ◽  
Clustering Techniques ◽  
Group Type ◽  
Enterprise Social Networks ◽  
Enterprise Social Network

With the proliferation of Enterprise Social Networks (ESN), the measurement of ESN activity becomes increasingly relevant. The emerging field of ESN analytics aims to develop metrics and models to measure and classify user activity to support organisational goals and outcomes. In this paper we focus on a neglected area of ESN analytics, the classification of activity in ESN groups. We engage in explorative research to identify a set of metrics that divides an ESN group sample into distinct types. We collaborate with Sydney-based service provider SWOOP Analytics who provided access to actual ESN meta data describing activity in 350 groups across three organisations. By employing clustering techniques, we derive a set of four group types: broadcast streams, information forums, communities of practice and project teams. We collect and reflect on feedback from ESN champions in fourteen organisations. For ESN analytics research we contribute a set of metrics and group types. For practice we envision a method that enables group managers to compare aspirations for their groups to embody a certain group type, with actual activity patterns.

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