Practical applications Social network analysis

1 practical applications

1.1 in computer-supported collaborative learning

1.1.1 key terms
1.1.2 unique capabilities
1.1.3 other methods used alongside sna

practical applications

social network analysis used extensively in wide range of applications , disciplines. common network analysis applications include data aggregation , mining, network propagation modeling, network modeling , sampling, user attribute , behavior analysis, community-maintained resource support, location-based interaction analysis, social sharing , filtering, recommender systems development, , link prediction , entity resolution. in private sector, businesses use social network analysis support activities such customer interaction , analysis, information system development analysis, marketing, , business intelligence needs. public sector uses include development of leader engagement strategies, analysis of individual , group engagement , media use, , community-based problem solving.

social network analysis used in intelligence, counter-intelligence , law enforcement activities. technique allows analysts map clandestine or covert organization such espionage ring, organized crime family or street gang. national security agency (nsa) uses clandestine mass electronic surveillance programs generate data needed perform type of analysis on terrorist cells , other networks deemed relevant national security. nsa looks 3 nodes deep during network analysis. after initial mapping of social network complete, analysis performed determine structure of network , determine, example, leaders within network. allows military or law enforcement assets launch capture-or-kill decapitation attacks on high-value targets in leadership positions disrupt functioning of network. nsa has been performing social network analysis on call detail records (cdrs), known metadata, since shortly after september 11 attacks.

large textual corpora can turned networks , analysed method of social network analysis. in these networks, nodes social actors, , links actions. extraction of these networks can automated, using parsers.

narrative network of elections 2012

the resulting networks, can contain thousands of nodes, analysed using tools network theory identify key actors, key communities or parties, , general properties such robustness or structural stability of overall network, or centrality of nodes. automates approach introduced quantitative narrative analysis, whereby subject-verb-object triplets identified pairs of actors linked action, or pairs formed actor-object.

in computer-supported collaborative learning

one of current methods of application of sna study of computer-supported collaborative learning (cscl). when applied cscl, sna used understand how learners collaborate in terms of amount, frequency, , length, quality, topic, , strategies of communication. additionally, sna can focus on specific aspects of network connection, or entire network whole. uses graphical representations, written representations, , data representations examine connections within cscl network. when applying sna cscl environment interactions of participants treated social network. focus of analysis on connections made among participants – how interact , communicate – opposed how each participant behaved on or own.

key terms

there several key terms associated social network analysis research in computer-supported collaborative learning such as: density, centrality, indegree, outdegree, , sociogram.

density refers connections between participants. density defined number of connections participant has, divided total possible connections participant have. example, if there 20 people participating, each person potentially connect 19 other people. density of 100% (19/19) greatest density in system. density of 5% indicates there 1 of 19 possible connections.
centrality focuses on behavior of individual participants within network. measures extent individual interacts other individuals in network. more individual connects others in network, greater centrality in network.

in-degree , out-degree variables related centrality.

in-degree centrality concentrates on specific individual point of focus; centrality of other individuals based on relation focal point of in-degree individual.
out-degree measure of centrality still focuses on single individual, analytic concerned out-going interactions of individual; measure of out-degree centrality how many times focus point individual interacts others.
a sociogram visualization defined boundaries of connections in network. example, sociogram shows out-degree centrality points participant illustrate outgoing connections participant made in studied network.

unique capabilities

researchers employ social network analysis in study of computer-supported collaborative learning in part due unique capabilities offers. particular method allows study of interaction patterns within networked learning community , can illustrate extent of participants interactions other members of group. graphics created using sna tools provide visualizations of connections among participants , strategies used communicate within group. authors suggest sna provides method of analyzing changes in participatory patterns of members on time.

a number of research studies have applied sna cscl across variety of contexts. findings include correlation between network s density , teacher s presence, greater regard recommendations of central participants, infrequency of cross-gender interaction in network, , relatively small role played instructor in asynchronous learning network.

other methods used alongside sna

although many studies have demonstrated value of social network analysis within computer-supported collaborative learning field, researchers have suggested sna not enough achieving full understanding of cscl. complexity of interaction processes , myriad sources of data make difficult sna provide in-depth analysis of cscl. researchers indicate sna needs complemented other methods of analysis form more accurate picture of collaborative learning experiences.

a number of research studies have combined other types of analysis sna in study of cscl. can referred multi-method approach or data triangulation, lead increase of evaluation reliability in cscl studies.

qualitative method – principles of qualitative case study research constitute solid framework integration of sna methods in study of cscl experiences.

ethnographic data such student questionnaires , interviews , classroom non-participant observations
case studies: comprehensively study particular cscl situations , relate findings general schemes
content analysis: offers information content of communication among members


quantitative method – includes simple descriptive statistical analyses on occurrences identify particular attitudes of group members have not been able tracked via sna in order detect general tendencies.

computer log files: provide automatic data on how collaborative tools used learners
multidimensional scaling (mds): charts similarities among actors, more similar input data closer together
software tools: quest, samsa (system adjacency matrix , sociogram-based analysis), , nud*ist






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