lnd.xprv/autopilot/prefattach.go

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