An Analytical Comparison of Social Network Measures

Guzman, Joshua D.; Deckro, Richard F.; Robbins, Matthew J.; Morris, James F.; Ballester, Nicholas

doi:10.1109/tcss.2014.2307451

Cited by 33 publications

(16 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The outcome is typical of networks in which members meet frequently with each other and have established multiple points of contact (Beritelli & Laesser, 2011). Actors in the DMK network are in closely linked clusters (Guzman, Deckro, Robbins, Morris, & Ballester, 2014) The relatively poor linkages across industries within the examined DMO may be of concern as ties between dissimilar actors help information flow across the network.…”

Section: Discussionmentioning

confidence: 99%

An examination of DMO network identity using Exponential Random Graph Models

Williams

Hristov

2018

Tourism Management

View full text Add to dashboard Cite

These studies were able to identify individuals and organizations that may be influential, but were not able to determine the extent of this influence. Whilst an emerging stream of tourism research has begun to employ inferential techniques, such as the Quadratic Assignment Procedure (Liu, Huang, & Fu, 2017), most Social Network Analysis (SNA) research relies on descriptions of networks to explain relationships among entities (Shumate & Palazzolo, 2010). However, these approaches do not enable researchers to determine if patterns identified in networks could have occurred by chance (Hunter & Handcock, 2006). Researchers have raised concerns when attempting to infer network characteristics from descriptive metrics; for example, clustering coefficient values, which indicate that entities or actors are important in networks, can be observed in randomly created networks (Newman, Strogatz, & Watts, 2001). This suggests these metrics will require additional qualitative or quantitative data about network actors or characteristics in order to support robust research. The aim of this paper is to examine the emergent network identity in a DMO network by identifying relational and node property influences on the structure of a communications network in a DMO. Using data collected from the Destination Milton Keynes initiative, the communication network of a DMO was modelled using an Exponential Random Graph approach. These models identified the extent to which node (organizational characteristics) and structure influence the distribution of communication ties in the network. Literature Review Network theory (Granovetter, 1973) and the analytical approach of SNA can be used to examine the arrangement of relationships between interacting entities, such as individuals, groups and organisations (Wang & Xiang, 2007). In the tourism and management domain, this perspective advocates that organisations no longer act solely as individual entities but through relational networks where value is created by initiating and nurturing collaboration (Fyall et al. 2009). SNA examines structural and relational properties of networks, such as density (Table 1), to identify patterns that can be used to explain social behaviour (Prell, 2012). SNA literature in business and management (Borgatti & Foster, 2003) seeks to demonstrate how the concept is able to visualise otherwise invisible social networks. Once depicted, invisible social networks, such as communication structures, may be leveraged for visible results in organisations (Conway, 2014). However, to date, little research has been undertaken to examine communication among destination organizations, particularly through the lens of SNA (Asero, Gozzo, & Tomaselli, 2016). SNA has often been perceived as a network tool that produces largely descriptive data without providing deeper insights (Prell 2012). Within this context, scholars have argued that social network studies often overemphasise the quantity of network relationships and interactions rather than their quality (Conway 2014). Table ...

show abstract

Section: Discussionmentioning

confidence: 99%

An examination of DMO network identity using Exponential Random Graph Models

Williams

Hristov

2018

Tourism Management

View full text Add to dashboard Cite

show abstract

“…This paper concentrates on a few commonly used approaches. For a more detailed review of topological measures of significance, the interested reader is directed to Newman, 59 Guzman et al, 60 and Scott. 61 A simple approach to characterizing the importance of networked entities is to simply consider the entity's degree.…”

Section: Vertex-level Topological Propertiesmentioning

confidence: 99%

A network perspective on assessing system architectures: Foundations and challenges

Potts

Sartor

Johnson

et al. 2019

Systems Engineering

View full text Add to dashboard Cite

Organizations are increasingly faced with the challenge of architecting complex systems that must operate within a System of Systems context. While network science has offered usefully clear insights into product and system architectures, we seek to extend these approaches to evaluate enterprise system architectures. Here, we explore the application of graph‐theoretic methods to the analysis of two real‐world enterprise architectures (a military communications system and a search and rescue system) and to assess the relative importance of different architecture components. For both architectures, different topological measures of component significance identify differing network vertices as important. From this, we identify several significant challenges a system architect needs to be cognisant of when employing graph‐theoretic approaches to evaluate architectures; finding suitable abstractions of heterogeneous architectural elements and distinguishing between network‐structural properties and system‐functional properties. These challenges are summarized as five guiding principles for utilizing network science concepts for enterprise architecture evaluation.

show abstract

“…3.3a Degree centrality: Node degree [22] information deals with the number of connections among the nodes in the network. The degree centrality for a node n 2 V is the fraction of nodes it is connected to and is denoted by deg n .…”

Section: Centrality Measuresmentioning

confidence: 99%

“…Closeness centrality: For a node n 2 V, closeness centrality of a node is the reciprocal of the sum of the shortest path distances from v to all other nodes. The closeness centrality of a node [22] is defined as…”

Section: 3bmentioning

confidence: 99%

See 1 more Smart Citation

Influence maximization in online social network using different centrality measures as seed node of information propagation

2019

View full text Add to dashboard Cite

Information propagation in the network is probabilistic in nature; simultaneously, it depends on the connecting paths of the propagation. Selection of seed nodes plays an important role in determining the levels and depth of the contagion in the network. This paper presents a comparative study when seed nodes for information propagation are selected through the properties of different centrality measures in the social network. This study captures the interaction measures of nodes in the social network, selects seed nodes based on five centrality measures, i.e. degree distribution, betweenness centrality, closeness centrality, Eigenvector and PageRank, and compares the affected nodes and levels of propagation within the network. We demonstrate the performance of the different centrality measures by processing three datasets of social network: Twitter network, Bitcoin network and author collaborative network. For the propagation of the information, we use breadth-first search (BFS) and susceptible-infectious-recovered (SIR) model and a detailed comparative study is also presented for each of the seed nodes selected using aforementioned network properties. Results show that the Eigenvector centrality and PageRank centrality measures outperform other centrality measures in all test cases in terms of propagation level and affected nodes during information propagation. Both Eigenvector and PageRank network data processing required a high computational overhead. For this reason we propose a hybrid model where using k-core the network is degenerated into a smaller network and centrality nodes are extracted from the smaller network. These centrality nodes, as compared to original centrality nodes, perform almost in the same manner in terms of influence maximization when k is chosen in a rational way.

show abstract

An Analytical Comparison of Social Network Measures

Cited by 33 publications

References 45 publications

An examination of DMO network identity using Exponential Random Graph Models

An examination of DMO network identity using Exponential Random Graph Models

A network perspective on assessing system architectures: Foundations and challenges

Influence maximization in online social network using different centrality measures as seed node of information propagation

Contact Info

Product

Resources

About