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Task trudges:
1. Compose an committer-to-committer tweet verge polish from the initiatory grounds regular, stocktwit_graph_input.csv.
Compose an verge polish from the initiatory grounds regular, stocktwit_graph_input.csv. We fair insufficiency span posts – commencement (Vertex 1) and target (Vertex 2) of an verge to compose a graph. Selecteded whole lines – tweets coercion posts K- “from_person” and M – “to_person” (or J and L coercion numerical committer IDs) and catch it as “stocktwit_from_to” or another designate you advance. 
2. Manifestation Gephi to propagate and catch committer (node) metrics. Selecteded the metrics you approve to ponder and manifestation coercion structure types later. Include at last 5 unanalogous metrics. Catch the metrics in a polish designated as stocktwit_node_yourname.csv. Submit this polish. Include answers to the coercionthcoming questions in HW6_yourname.doc coercion dependence.
a. Which three committers own the main betweenness centrality? 
b. Which three committers own the main entirety quality? 
c. Which three committers own the main influence?
3. Elevate the Node Consideration coercion Coercionetellion
(1). Open the stocktwit_node.csv polish in Yield, and compose a newlightlight capricious: Fitted (i.e. suggested). It is the target capriciouss we manage to systematize or coercionetell.
(2). Do referable obstruct the stocktwit_node.csv polish. Open the stocktwit_graph_input.csv polish. And then go to the stocktwit_node.csv.
(3). Referablee that the specific in the stocktwit_node.csv polish is a node (i.e. each specific committer) and the specific in the stocktwit_graph_input.csv polish is a tweet (i.e. each communication). So, in appoint to make-over the estimate of fitted from the consideration of stocktwit_graph_input to the stocktwit_node consideration, we insufficiency to do grounds intercharge.
To Fitted, we insufficiency to appoint single estimate to single committer (i.e. whether they are fitted or referable – 1 stands coercion yes; 0 stands coercion no.).
Manifestation the VLOOKUP operation to appoint the estimate of “suggested” from the consideration of stocktwit_graph_input to the post, “Expert”, in stocktwit_node consideration. The operation coercion the restraintemost line should be approve this:
= VLOOKUP(A2, stocktwit_graph_input.csv!$K$1:$AB$38200,18,FALSE),
where “A2” is the node designate; “stocktwit_graph_input.csv!$K$1:$AB$38200” is the consideration stroll we seem up; 18 is the post reckon from the consideration stroll that we manage to yield the estimate, “FALSE” stands coercion an equitable tally of the estimate.
(4). Catch the stocktwit_node.csv polish. BTW, you can delete those lines who own restraintfeiture estimate in Fitted, becamanifestation these nodes simply show in the “to_person” post, they do referable own tweets.
Manifestation strain operation in yield to transport the #NAs.
4. In R, elevate and evaluate a description type that manifestations the metrics in stocktwit_node_yourname.csv from trudge 2 as features to systematize committers into “expert” stocktwit committer (i.e., “suggested”=1)” or referable (“suggested”=0) which is the target engrave capricious. 
(1). Using a root of 100, randomly selecteded 60% of the lines into grafting (e.g. designated traindata). Divide the other 40% of the lines evenly into span holdout test/validation regulars (e.g., designated testdata1 and testdata2).
(2). Elevate the tree using the C50 operation with want regulartings.
(3). Propagate coercionetellions (i.e. estimations) of the estimates of the target capricious coercion the testing instances.
Propagate a indistinctness matrix that shows the counts of gentleman-positive, gentleman-negative, false-unequivocal and false-negative coercionetellions coercion twain testdata1 and testdata2. Consider 1 as unequivocal arrange.
Propagate seven accomplishment metrics – Accuracy (percent of whole correctly arrangeified testing instances), and accuracy (percent of instances coercionetelled to own a arrange are servile), recwhole (too gentleman unequivocal) and F-measure (too F-score) of the span arrangees of fitted. 
(4). Would you commend using the features from network dissection to realize fitteds in the Stocktwit nationality? Why or why referable? Include answers to the coercionthcoming questions in HW6_yourname.doc coercion dependence.

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