94 lines
2.9 KiB
Go
94 lines
2.9 KiB
Go
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package autopilot
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import (
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"runtime"
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"github.com/btcsuite/btcutil"
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)
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// TopCentrality is a simple greedy technique to create connections to nodes
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// with the top betweenness centrality value. This algorithm is usually
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// referred to as TopK in the literature. The idea is that by opening channels
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// to nodes with top betweenness centrality we also increase our own betweenness
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// centrality (given we already have at least one channel, or create at least
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// two new channels).
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// A different and much better approach is instead of selecting nodes with top
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// centrality value, we extend the graph in a loop by inserting a new non
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// existing edge and recalculate the betweenness centrality of each node. This
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// technique is usually referred to as "greedy" algorithm and gives better
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// results than TopK but is considerably slower too.
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type TopCentrality struct {
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centralityMetric *BetweennessCentrality
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}
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// A compile time assertion to ensure TopCentrality meets the
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// AttachmentHeuristic interface.
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var _ AttachmentHeuristic = (*TopCentrality)(nil)
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// NewTopCentrality constructs and returns a new TopCentrality heuristic.
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func NewTopCentrality() *TopCentrality {
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metric, err := NewBetweennessCentralityMetric(
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runtime.NumCPU(),
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)
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if err != nil {
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panic(err)
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}
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return &TopCentrality{
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centralityMetric: metric,
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}
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}
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// Name returns the name of the heuristic.
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func (g *TopCentrality) Name() string {
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return "top_centrality"
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}
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// NodeScores will return a [0,1] normalized map of scores for the given nodes
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// except for the ones we already have channels with. The scores will simply
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// be the betweenness centrality values of the nodes.
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// As our current implementation of betweenness centrality is non-incremental,
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// NodeScores will recalculate the centrality values on every call, which is
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// slow for large graphs.
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func (g *TopCentrality) NodeScores(graph ChannelGraph, chans []Channel,
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chanSize btcutil.Amount, nodes map[NodeID]struct{}) (
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map[NodeID]*NodeScore, error) {
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// Calculate betweenness centrality for the whole graph.
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if err := g.centralityMetric.Refresh(graph); err != nil {
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return nil, err
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}
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normalize := true
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centrality := g.centralityMetric.GetMetric(normalize)
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// Create a map of the existing peers for faster filtering.
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existingPeers := make(map[NodeID]struct{})
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for _, c := range chans {
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existingPeers[c.Node] = struct{}{}
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}
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result := make(map[NodeID]*NodeScore, len(nodes))
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for nodeID := range nodes {
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// Skip nodes we already have channel with.
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if _, ok := existingPeers[nodeID]; ok {
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continue
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}
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// Skip passed nodes not in the graph. This could happen if
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// the graph changed before computing the centrality values as
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// the nodes we iterate are prefiltered by the autopilot agent.
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score, ok := centrality[nodeID]
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if !ok {
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continue
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}
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result[nodeID] = &NodeScore{
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NodeID: nodeID,
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Score: score,
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}
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}
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return result, nil
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}
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