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Not the type of Graph you're thinking of (Brook...

Not the type of Graph you're thinking of (BrooklynJS June 2014)

Not the type of Graph you're thinking of. Graph theory, npm, codependencies and friends.

Charlie Robbins

June 20, 2014
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  1. And why should I pay attention? 8IPBSFZPVFYBDUMZ Founder & CEO

    at RECRUITERS!!! ACTUALLY HAS FIVE YEARS OF EXPERIENCE WITH
  2. It’s kind of like math. Yes, math. 5IF( 7 &

    LJOEPG(SBQI Definition 1. A directed graph consists of a finite set of vertices V and a set of directed edges E. An edge is an ordered pair of vertices (u,v). Vertex Vertex Edges Edges
  3. The math will be over soon. I promise 5IF( 7

    & LJOEPG(SBQI • A path is a sequence of vertices (x1 , x2 , . . . , xn ) such that consecutive vertices are adjacent (edge (xi , xi + 1) ∈ E for all 1 ≤ i ≤ n − 1). • A path is simple when all vertices are distinct. • A cycle is a simple path that ends where it starts, that is, xn = x1 . • The distance between between u and v is the length of the shortest path from u to v. • An undirected graph is connected when there is a path between every pair of vertices.
  4. The math will be over soon. I promise 5IF( 7

    & LJOEPG(SBQI • The connected component of a node u in an undirected graph is the set of all nodes in the graph reachable by a path from u. • A directed graph is strongly connected when, for every pair of vertices u, v, there is a path from u to v and a path from v to u. • The strongly connected component of a node u in a directed graph is the set of nodes v such that there is a path from u to v and a path from v to u.
  5. Last set of definitions and notation 5IF( 7 & LJOEPG(SBQI

    • |V| = n = The number of verticies • |E| = m = The number of edges • A graph algorithm is linear when it runs in O(|V | + |E|) = O(n + m).
  6. Two common internal representations (SBQIT)PXEPUIFZXPSL • Adjacency list: A graph

    with edges |E| = m can be represented as a list of m adjacency pairs. • Adjacency matrix: A graph of size |V| = n can be represented as an n x n matrix. Question: How do we represent the edges?
  7. Breadth First Search & Depth First Search (SBQI4FBSDI"MHPSJUINT The Question.

    does the graph G contain an s-t path? i.e. Does a path exist between two verticies, s & t, in the graph G? s t G
  8. Breadth First Search (SBQI4FBSDI"MHPSJUINT The main idea of BFS is

    to explore the graph starting from a vertex s outward in all possible directions, adding nodes one “layer” at a time.
  9. Depth First Search (SBQI4FBSDI"MHPSJUINT 1. Start at s, and try

    the first edge out of s, towards some node v. 2. Continue from v until you reach a “dead end”, that is, a node whose neighbors have all been explored. 3. Backtrack to the first node with an unexplored neighbor and repeat 2.
  10. DFS makes your graph a tree! Well .... sort of.

    "HSBQIJTBUSFF 1. Forward edges: go from an ancestor to a descendant other than a child. 2. Back edges: go from a descendant to an ancestor, other than the parent. 3. Cross edges: go from right to left, that is, • from tree to tree; e.g., (v5 ,v4 ) in Figure 2(b). • between nodes in the same tree but in different branches. e.g., (u, v) on the right of Figure 3(b).
  11. All your paths are belong to s-t. Wait what? 1BUI'JOEJOH"MHPSJUINT

    The Question. what are all of the shortest paths in G from s? i.e. What is the shortest path from a single source, s, and to all other verticies in G? By knowing an answer to this, we also know: • Single-pair shortest-path: What is the shortest s-t path in G? • Single-destination shortest-path: what are all of the shortest paths in G to a single sink, t? • All-pairs shortest-paths: What is the shortest path between all (u,v) vertex pairs in G?
  12. The more you know. (SBQI"MHPSJUINT Did you know that the

    collective form of computer scientists is ...? a dijkstra! Seriously tho. Dijkstra was a boss. He created an algorithm to solve this for us. Here is how it works.
  13. Dijkstra’s algorithm and friends. 1BUI'JOEJOH"MHPSJUINT Start with your single source,

    s And a set of shortest paths from s for vertices, Q Initially, Q only contains s, with distance(s) = 0 In every iteration, examine V - Q and select vertex v: 1. has an incoming edge from some vertex ∈ Q 2. minimizes the quantity among all v ∈ V − Q d(v) = min distance(u) + weight(u, v) u ∈ Q
  14. An application of graphs in “real” life! %FQFOEFODZHSBQIT • Basic

    graph representation is not extremely helpful for visualizing package relationships. • But it does provide a basic structure for a graph search problem.
  15. The G = (V,E) kind of graph %FQFOEFODZHSBQIT • We

    then add a colored edge from any node nA to nB if package A depends on package B. • Edges are colored by dependency type: dependencies or devDependencies • To build our graph, G, we add a node (or vertex) for every package. na nb nc
  16. The G = (V,E) kind of graph %FQFOEFODZHSBQIT na nb

    nc { "name": "package-a", "dependencies": { "package-b": "~1.0.4", "package-c": "~2.1.3" }, "main": "./index.js" } • Now imagine this graph for 70,000+ modules.
  17. Ok, so what is this useful for? %FQFOEFODZHSBQIT • There

    are tons of useful applications of dependency graphs. • Lets consider one. Codependencies
  18. Well, technically co(*)dependencies. $PEFQEFODJFT • Codependencies answer the question “people

    who depend on package A also depend on ...” ["package", "codep", "thru"] $PVDI%#
  19. The meat of the analysis $PEFQEFODJFT • So this is

    all well and good, but what the heck are you doing?!?! For module NAME generate a matrix by: - Rank codependencies based on number of times they appear - For each codependency C in the SET of the top N: Rank SET - {C} by number of times they appear to create ROW[C]
  20. Understanding codependencies through winston $PEFQEFODJFT • This last step yields

    a matrix for the codependency relationship: ┌───────┬───────────┬──────────┬──────────┬──────────┬───────────┐ │ │ asyn… │ expr… │ opti… │ requ… │ unde… │ ├───────┼───────────┼──────────┼──────────┼──────────┼───────────┤ │ asyn… │ 0.0000 │ 553.0000 │ 534.0000 │ 837.0000 │ 1359.0000 │ ├───────┼───────────┼──────────┼──────────┼──────────┼───────────┤ │ expr… │ 553.0000 │ 0.0000 │ 314.0000 │ 365.0000 │ 648.0000 │ ├───────┼───────────┼──────────┼──────────┼──────────┼───────────┤ │ opti… │ 534.0000 │ 314.0000 │ 0.0000 │ 335.0000 │ 448.0000 │ ├───────┼───────────┼──────────┼──────────┼──────────┼───────────┤ │ requ… │ 837.0000 │ 365.0000 │ 335.0000 │ 0.0000 │ 786.0000 │ ├───────┼───────────┼──────────┼──────────┼──────────┼───────────┤ │ unde… │ 1359.0000 │ 648.0000 │ 448.0000 │ 786.0000 │ 0.0000 │ └───────┴───────────┴──────────┴──────────┴──────────┴───────────┘
  21. Understanding codependencies through winston $PEFQEFODJFT • Now we need to

    weight the matrix based on the overall appearance of these codependencies 240 | async | 0.2761 207 | underscore | 0.2382 163 | express | 0.1875 133 | request | 0.1530 126 | optimist | 0.1449 869 total
  22. Reading the tea leaves of dense data visualization $PEFQEFODJFT •

    The size of the arc represents the degree of the codependency relationship with the parent module. • The size of the chord represents the degree of the codependency relationship between each pair. • The color of the chord represents the “dominant” module between the pair. winston
  23. I CAN HAZ MORE GRAPHS? • Dijkstra fails under some

    conditions • Minimum Spanning Trees • Prim’s Algorithm • Kruskal’s Algorithm • Bipartite Matching Graphs • Flow Networks & min s-t cuts • Ford-Fulkerson • Edmonds Karp