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| ## --- Day 23: LAN Party --- | ||
| As The Historians wander around a secure area at Easter Bunny HQ, you come across posters for a [LAN party](https://en.wikipedia.org/wiki/LAN_party) scheduled for today! Maybe you can find it; you connect to a nearby _datalink port_ and download a map of the local network (your puzzle input). | ||
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| The network map provides a list of every <em>connection between two computers</em>. For example: | ||
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| _Visit the website for the full story and [full puzzle](https://adventofcode.com/2024/day/23) description._ | ||
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| We tackled a graph algorithm problem today, where we had to find maximal cliques in an undirected graph. The literature provides [efficient algorithms](https://en.wikipedia.org/wiki/Bron%E2%80%93Kerbosch_algorithm) for this problem, but our graph is not too large, so we can use a straightforward "poor man's" strategy as well. | ||
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| I started by creating _seed_ components containing nodes that start with '_t_'. Then, I proceeded to _grow_ these components by adding a single node to them in all possible ways. First, I identify the neighbors of each _t_ node, followed by the triples which is required to solve Part 1. In Part 2, this process is continued until a single maximal component remains. |
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| namespace AdventOfCode.Y2024.Day23; | ||
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| using System; | ||
| using System.Collections.Generic; | ||
| using System.Collections.Immutable; | ||
| using System.Linq; | ||
| using Graph = System.Collections.Generic.Dictionary<string, System.Collections.Generic.HashSet<string>>; | ||
| using Component = string; | ||
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| [ProblemName("LAN Party")] | ||
| class Solution : Solver { | ||
| public object PartOne(string input) { | ||
| var g = GetGraph(input); | ||
| var components = GetSeed(g); | ||
| components = Grow(g, components); | ||
| components = Grow(g, components); | ||
| return components.Count; | ||
| } | ||
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| public object PartTwo(string input) { | ||
| var g = GetGraph(input); | ||
| var components = GetSeed(g); | ||
| while (components.Count > 1) { | ||
| components = Grow(g, components); | ||
| } | ||
| return components.Single(); | ||
| } | ||
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| HashSet<Component> GetSeed(Graph g) => g.Keys.Where(k=>k.StartsWith("t")).ToHashSet(); | ||
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| HashSet<Component> Grow(Graph g, HashSet<Component> components) => ( | ||
| from c in components | ||
| let members = Members(c) | ||
| from neighbour in members.SelectMany(m => g[m]).Distinct() | ||
| where !members.Contains(neighbour) | ||
| where members.All(m => g[neighbour].Contains(m)) | ||
| select Extend(c, neighbour) | ||
| ).ToHashSet(); | ||
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| IEnumerable<string> Members(Component c) => | ||
| c.Split(","); | ||
| Component Extend(Component c, string item) => | ||
| string.Join(",", Members(c).Append(item).OrderBy(x=>x)); | ||
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| Graph GetGraph(string input) { | ||
| var edges = | ||
| from line in input.Split("\n") | ||
| let nodes = line.Split("-") | ||
| from edge in new []{(nodes[0], nodes[1]), (nodes[1], nodes[0])} | ||
| select (From: edge.Item1, To: edge.Item2); | ||
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| return ( | ||
| from e in edges | ||
| group e by e.From into g | ||
| select (g.Key, g.Select(e => e.To).ToHashSet()) | ||
| ).ToDictionary(); | ||
| } | ||
| } |
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| 1368 | ||
| dd,ig,il,im,kb,kr,pe,ti,tv,vr,we,xu,zi |
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