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Taxonomy of Routing Protocols for Opportunistic Networks
Published in Khaleel Ahmad, Nur Izura Udzir, Ganesh Chandra Deka, Opportunistic Networks, 2018
Khaleel Ahmad, Muneera Fathima, Khairol Amali bin Ahmad
Routing in DTN is a challenge due to its dynamic nature. DTN uses the store-carry-forward approach for routing, in which packet is kept at the node until it encounters another node. In the past few years, social-based routing protocol has been given much interest. In social-based routing protocol, we are exploiting the social behavior of the nodes to make better decisions. Social network analysis is a study which mainly focuses on the relation between social entities, patterns and the implications of their relationships. SimBet, a social-based routing protocol, is proposed, which works on similarity and betweenness centrality for routing packets (Patel & Gondaliya, 2015). The SimBet multi-copy routing scheme is introduced, which sends multiple copies of messages during encounter opportunity depend on the proportion of the SimBet utility value of the nodes which utilize the consent of EBR. Simulation results show that SimBet multi-copy outperforms both SimBet and epidemic in terms of delivery ratio, with lower overhead and latency for large buffer space and time to live (TTL).
Social network analysis
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Social network analysis is primarily a quantitative method that borrows from graph theory (the theoretical study of graphs, their structure and application) and sociometry (the study and measurement of relationships within a group of people). It also borrows from qualitative methods used by ethnographers interested in kinship, friendship, communities and interpersonal relations. If you are interested in finding out more about qualitative social network analysis, Edwards (2010) provides an interesting and enlightening methodological discussion, describing qualitative methods such as walking interviews, participatory mapping, observation, network maps and in-depth interviewing.
Social Networks
Published in Vivek Kale, Agile Network Businesses, 2017
Social network analysis is used to understand the social structure that exists among entities in an organization. The defining feature of SNA is its focus on the structure of relationships, ranging from causal acquaintances to close bonds. This is in contrast to other areas of the social sciences, where the focus is often on the attributes of agents rather than on the relations between them. SNA maps and measures formal and informal relationships to understand what facilitates or impedes the knowledge flows that bind the interacting units—that is, who knows whom, and who shares what information and how. Social network analysis is focused on uncovering the patterning of people’s interactions. SNA is based on the intuition that these patterns are important features of the lives of the individuals who display them. Network analysts believe that how an individual lives depends chiefly on how that individual is tied into a larger web of social connections. Moreover, many believe that the success or failure of societies and organizations often depends on the patterning of their internal structure, which is guided by formal concept analysis, which is grounded in the systematic analysis of empirical data. With the availability of powerful computers and discrete combinatorics (especially graph theory) after 1970, the study of SNA took off as an interdisciplinary specialty, the applications of which are found in many fields including organizational behavior, interorganizational relations, the spread of contagious diseases, mental health, social support, the diffusion of information, and animal social organization. SNA software provides the researcher with data that can be analyzed to determine the centrality, betweenness, degree, and closeness of each node.
Diffusing innovative road safety practice: A social network approach to identifying opinion leading U.S. cities
Published in Traffic Injury Prevention, 2018
Seth LaJeunesse, Stephen Heiny, Kelly R. Evenson, Lisa M. Fiedler, Jill F. Cooper
To be included in the network analysis, participants must have provided their job title, organizational affiliation, and the nomination of at least one municipality outside of the respondent’s own municipality as a source of advice. We then created an adjacency matrix for the intermunicipal networks in each of the major U.S. census regions (i.e., Northeast, South, Midwest, West), in which 1 indicated that a social modeling relationship existed between 2 municipalities and 0 indicated the absence of a relationship. The matrix was constructed such that the ego in the dyad was the advice seeker (i.e., the respondent’s municipality) and the alter was the advice source or model (i.e., the municipality that the respondent identified as a model of transportation safety). We then used the adjacency matrix for the network analysis at the whole-network level using Gephi (Ver. 0.9.2 Gephi Consortium 2017), an open-source social network analysis and visualization software package. Descriptive statistics were performed at whole-network and nodal (i.e., municipal) levels using Stata (Ver. 15; Stata, College Station, TX).
Correlative failure analysis of CNC equipment based on SNA
Published in Production & Manufacturing Research, 2021
Shuguang Sun, Yahui Han, Meng Zhang, Xiyu Liu, Guixiang Shen, Xin Guan
A social network is a collection that includes multiple nodes (social actors) and connections between the nodes (relationships between the actors) (Liu & Wang, 2016). Social network analysis mainly studies and analyzes the relationships in the network to obtain structural information of the network, and is a methodology of social network theory. It is the methodology of social network theory (Chatfield,Reddick,& Brajawidagda,2015; Bourbousson et al., 2015; Jia et al., 2013; Ye et al., 2015; Zhong & Peng, 2015).
The past, present, and future of network monitoring: A panel discussion
Published in Quality Engineering, 2021
Nathaniel T. Stevens, James D. Wilson
Almost 200 years later, psychiatrist Jacob Moreno introduced the roots of modern-day social network analysis with his first-time use of hand-drawn networks, then called “sociograms,” to analyze and represent the relationships between school children in his book Who Shall Survive? in 1934. Social network analysis, which seeks to model and understand interactions and communications among individuals, has driven and continues to drive much of the research done in network analysis to date.