How can the graph filtering algorithm be accelerated with a large number of vertices and events?

To speed up the graph filtering algorithm with a large number of vertices and events, several approaches can be used:

1. Using efficient data structures — for example, adjacency list instead of adjacency matrix for sparse graphs.
2. Parallelization — distribute the processing of events and filtering across parts of the graph using goroutines.
3. Indexing and caching — store intermediate filtering results to avoid recalculating the same data.
4. Pruning based on conditions — apply filters as early as possible to reduce the number of vertices processed.
5. Using algorithms with lower complexity — for example, breadth-first search (BFS) or depth-first search (DFS) with optimizations.

An example of parallel filtering using goroutines:

func filterGraph(nodes []Node, filterFunc func(Node) bool) []Node {
    var wg sync.WaitGroup
    ch := make(chan Node, len(nodes))

    for _, node := range nodes {
        wg.Add(1)
        go func(n Node) {
            defer wg.Done()
            if filterFunc(n) {
                ch <- n
            }
        }(node)
    }

    go func() {
        wg.Wait()
        close(ch)
    }()

    var result []Node
    for n := range ch {
        result = append(result, n)
    }
    return result
}

This approach allows utilizing all available CPUs and speeds up filtering.