Changes

*There exists many Many R packages dedicated include social media analysis functionality*The advantage of using R, instead of a blackbox nice-UI, is R's portability and flexibility. Data can move easily between packages and into other software such as MSExcel or SPSS (Statistical Package for the Social Sciences).*According to mapping twitter networksthe R community, it is widely held that despite their difference in specific functionalities, one can achieve all basic operations and visualization needs with any one of these R packages*Unlike NodeXLFor all R-based analysis, we have to use our in-house twitter webcrawler [[Twitter Webcrawler (Tool)]] to grab raw data from twitter, and another convert them into appropriate structures for R consumption '''(yet to be foundunsure)''' library to process twitter data into lists of nodes and edges, depending on the questions that we want to ask. (e.g. go through 20,000 tweets and form an edge for every mention)**'''igraph'''***PowerfulTypically, feature-rich library for network analysis***[https://github.com/igraph/igraph igraph on Github]***[http://igraph.org/ igraph on its own domain]***Available for '''R'''they are all OOP with graphs, '''Py''' nodes and '''C'''edges as objects

===Features, from a python POVand Review=======igraph====*OOP with graphsPowerful, nodes and edges as objectsfeature-rich library *https://github.com/igraph/igraph igraph on Github]*Nodes <code>g.vs()<[http://code> and edges <code>gigraph.es()<org/code> can take igraph on user-assigned attributes, each one easily retrieveable one call awayits own domain]*Also available for '''Py''' and '''C'''*Graphs <code>g</code> has Known for ease of calculating basic graph properties metrics such as:***<code>g.edge_betweeness()</code>***<code>g.degree()</code>***<code>g.pagerank()</code>***<code>g.betweenness()</code>***<code>g.select()</code> to enable easy node/edge selection*InKnown for possessing community detection algorithm (e.g. Newman-Girvan) ===statnet===*Implements recent advances in statistical modelling of networks - ''unsure if we need such high levels of sophistication in graph theory implementation''.*Focuses on '''statistical modelling''' of network data*Includes libraries <code>network</code>, <code>sna</code> which stands for naturally, '''Social Media Analysis'''**3-built layoutD graph plot**Subgraph census routines, including component information, paths/cycles/cliques, removing isolates**Positional Analysis*Unlike igraph, statnet is developed by a team of statisticians from the University of Washington. It is thus heavy on the statistical analysis side.**ERGMs model***Exponential family Random Graph Models***Advanced technique associated with analyzing data esp. in social networks***Statistical model operates on the premise that all alternative networks are to be considered as much as the observed one. Alternative networks are, for e.g., generated through the ''Degree Preserving Randomization'' method.**Includes tools for model estimation, model evaluation, model-based network simulation, and network visualization. ***Broad functionalities powered by central MCMC (Markov Chain Monte Carlo) algorithm ===Others===*'''tnet'''**Two-mode networks ''(i.e. rows and columns of a two-mode matrix are different entities; e.g. persons vs. organizations)''*'''RSiena'''**Actor-oriented model of network dynamics***Extremely theoretical and, presently, academic discipline.***Addresses the very realistic question of networks as an evolving system driven by actors (nodes of twitter users, in our case). ***Stochastic; statistical modelling, Markov Chain**DREAM CASE:***Could we use this modelling technique to predict future twitter trends of a the entrepreneurship interest group?