153 lines
4.6 KiB
Go
153 lines
4.6 KiB
Go
package autopilot
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import (
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prand "math/rand"
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"time"
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"github.com/btcsuite/btcd/btcec"
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"github.com/btcsuite/btcutil"
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)
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// PrefAttachment is an implementation of the AttachmentHeuristic interface
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// that implement a non-linear preferential attachment heuristic. This means
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// that given a threshold to allocate to automatic channel establishment, the
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// heuristic will attempt to favor connecting to nodes which already have a set
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// amount of links, selected by sampling from a power law distribution. The
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// attachment is non-linear in that it favors nodes with a higher in-degree but
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// less so than regular linear preferential attachment. As a result, this
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// creates smaller and less clusters than regular linear preferential
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// attachment.
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//
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// TODO(roasbeef): BA, with k=-3
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type PrefAttachment struct {
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}
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// NewPrefAttachment creates a new instance of a PrefAttachment heuristic.
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func NewPrefAttachment() *PrefAttachment {
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prand.Seed(time.Now().Unix())
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return &PrefAttachment{}
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}
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// A compile time assertion to ensure PrefAttachment meets the
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// AttachmentHeuristic interface.
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var _ AttachmentHeuristic = (*PrefAttachment)(nil)
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// NodeID is a simple type that holds an EC public key serialized in compressed
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// format.
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type NodeID [33]byte
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// NewNodeID creates a new nodeID from a passed public key.
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func NewNodeID(pub *btcec.PublicKey) NodeID {
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var n NodeID
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copy(n[:], pub.SerializeCompressed())
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return n
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}
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// Name returns the name of this heuristic.
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//
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// NOTE: This is a part of the AttachmentHeuristic interface.
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func (p *PrefAttachment) Name() string {
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return "preferential"
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}
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// NodeScores is a method that given the current channel graph and current set
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// of local channels, scores the given nodes according to the preference of
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// opening a channel of the given size with them. The returned channel
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// candidates maps the NodeID to a NodeScore for the node.
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//
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// The heuristic employed by this method is one that attempts to promote a
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// scale-free network globally, via local attachment preferences for new nodes
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// joining the network with an amount of available funds to be allocated to
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// channels. Specifically, we consider the degree of each node (and the flow
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// in/out of the node available via its open channels) and utilize the
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// Barabási–Albert model to drive our recommended attachment heuristics. If
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// implemented globally for each new participant, this results in a channel
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// graph that is scale-free and follows a power law distribution with k=-3.
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//
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// The returned scores will be in the range [0.0, 1.0], where higher scores are
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// given to nodes already having high connectivity in the graph.
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//
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// NOTE: This is a part of the AttachmentHeuristic interface.
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func (p *PrefAttachment) NodeScores(g 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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// Count the number of channels for each particular node in the graph.
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var maxChans int
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nodeChanNum := make(map[NodeID]int)
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if err := g.ForEachNode(func(n Node) error {
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var nodeChans int
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err := n.ForEachChannel(func(_ ChannelEdge) error {
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nodeChans++
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return nil
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})
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if err != nil {
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return err
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}
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// We keep track of the highest-degree node we've seen, as this
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// will be given the max score.
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if nodeChans > maxChans {
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maxChans = nodeChans
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}
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// If this node is not among our nodes to score, we can return
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// early.
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nID := NodeID(n.PubKey())
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if _, ok := nodes[nID]; !ok {
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return nil
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}
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// Otherwise we'll record the number of channels.
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nodeChanNum[nID] = nodeChans
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return nil
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}); err != nil {
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return nil, err
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}
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// If there are no channels in the graph we cannot determine any
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// preferences, so we return, indicating all candidates get a score of
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// zero.
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if maxChans == 0 {
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return nil, nil
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}
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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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// For each node in the set of nodes, count their fraction of channels
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// in the graph, and use that as the score.
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candidates := make(map[NodeID]*NodeScore)
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for nID, nodeChans := range nodeChanNum {
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_, ok := existingPeers[nID]
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switch {
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// If the node is among or existing channel peers, we don't
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// need another channel.
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case ok:
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continue
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// If the node had no channels, we skip it, since it would have
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// gotten a zero score anyway.
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case nodeChans == 0:
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continue
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}
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// Otherwise we score the node according to its fraction of
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// channels in the graph, scaled such that the highest-degree
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// node will be given a score of 1.0.
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score := float64(nodeChans) / float64(maxChans)
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candidates[nID] = &NodeScore{
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NodeID: nID,
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Score: score,
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}
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}
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return candidates, nil
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}
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