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    <p>Your data structure can be made to work, but probably not efficiently. And it will be a lot of work.</p> <p>With your current data structure you can use an A* search (see <a href="https://en.wikipedia.org/wiki/A" rel="nofollow">https://en.wikipedia.org/wiki/A</a>*_search_algorithm for a basic description) to find a path to the point, which necessarily finds a neighbor. Then pretend that you've got a little guy at that point, put his right hand on the wall, then have him find his way clockwise around the point. When he gets back, he'll have found the rest.</p> <p>What do I mean by find his way clockwise? For example suppose that you go Down from the neighbor to get to his point. Then your guy should be faced the first of Right, Up, and Left which he has a neighbor. If he can go Right, he will, then he will try the directions Down, Right, Up, and Left. (Just imagine trying to walk through the maze yourself with your right hand on the wall.)</p> <p>This way lies insanity.</p> <p>Here are two alternative data structures that are much easier to work with.</p> <p>You can use a quadtree. See <a href="http://en.wikipedia.org/wiki/Quadtree" rel="nofollow">http://en.wikipedia.org/wiki/Quadtree</a> for a description. With this inserting a node is logarithmic in time. Finding neighbors is also logarithmic. And you're only using space for the data you have, so even if your graph is very spread out this is memory efficient.</p> <p>Alternately you can create a class for a type of array that takes both positive and negative indices. Then one that builds on that to be 2-d class that takes both positive and negative indices. Under the hood that class would be implemented as a regular array and an offset. So an array that can start at some number, positive or negative. If ever you try to insert a piece of data that is before the offset, you create a new offset that is below that piece by a fixed fraction of the length of the array, create a new array, and copy data from the old to the new. Now insert/finding neighbors are usually <code>O(1)</code> but it can be very wasteful of memory.</p>
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    1. POHow to find the neighbors of a graph effiiciently
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    1. COI like your answer as well...but unfortunately I could only pick one as the best....
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