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Nodes can have particular attributes or categories attached to them as can edges. Thickness, colors, size etc.

Because there's there’s no axis, the space between nodes is not meaningful, only the number of edges separating nodes determines "space“space." ” Network graphs take a "force directed" “force directed” layout so the same visualization can take many different forms but meaning stays constant. "Force directed" “Force directed” is random, but generated so that edges act as springs, nodes are where springs are attached, laid out so that springs have least amount of tension.

Pitfall of basic network graph is lack of change over time, not so useful for a historian. Basic way to fix this is by having an "aggregate “aggregate static network" network” where all time information is coded and included in one static graph, "this “this network over 100 years looks like X." ” Dynamic Networks aggregate overtime, nodes that are there, stay there: time slices 1800-1850, 1800-1900, 1800-1950 etc. Or Sliding scale, shows increments; network every 10 or 5 years. At some point you can't can’t make time smaller b/c network ceases to exist. You have to aggregate at least somewhat.

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Triad: useful for modeling transitivity. Or "global “global clustering coefficient" coefficient” of the amount of triads, how many are completed triangles.

Edges: directed or undirected edges. Directed network would be like a letter writing network. A can write to B but B doesn't doesn’t necessarily write to A. Can show unreciprocated connections.

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Bipartite Networks: Many network tools (like Gephi) not really equipped to deal with these. It's It’s a network with 2 types of nodes. IE Books and authors. 1-4 authors, A-K books

These networks work on basic network tool, but what you get out of it isn't isn’t perfectly clear. Most often when using these tools you will want to build unipartite graphs.

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Closeness Centrality: How close is any node to any other node? Think, "who “who do you have to tell to spread info the farthest the fastest?"”

Local clustering: how many of my friends are friends (connected) with each other. This goes hand in hand with modularity, think about "communities" or "groups of friends"

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Preferential attachment: if you have a lot of connections, you're you’re more able/likely to get more connections. Most social networks (Republic of Letters) you have a few people with lots of connections holding the network together, most people don't don’t have that many.

Modularity: groups, shared edges (33:20ish) automated, algorithmic ways to detect communities.

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