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Ranked influence typology

Davide Miceli edited this page Nov 14, 2018 · 20 revisions

Description

Detect the type of influence that each node holds within a network.

The algorithm detects not only the influence played by every node inside a network, but also how it contributes to the overall network (for example the word-of-mouth, content creation, and diffusion of information).

Given a large dataset of connections as input, it provides a ranking of all nodes by influence score, reporting their typology of influence. Every node plays a certain role in the network and affects the other nodes in its own different way.

Typologies of influence

Ranked Influence Typology algorithm is capable to detect 19 categories of influencers.

Role Description Graph view
Amplifier A node that talks more than listens. It increases the flow of information. amplifier
Blackhole When the node absorbs only a huge amount of information and does not emit anything. This node is totally inactive in producing information. blackhole
Bridge It is a node that ties together two hubs allowing a direct passage of information. bridge
Chain It is a link in a directed word-of-mouth chain. What it receives will be passed to the next unique connected node. chain
Channeler When a node directs the information channeled by a reducer or by a dam into a chain. channeler
Connector A node that only connects together two common nodes. connector
Dam It listens too much but talks very little. It may also act as an information selector, collecting a lot of information to share what is the most relevant. It could be a big barrier to the spread of news too. dam
Emitter It does not listen, it is only a source of information. emitter

How to use it

watchcomplexity.influence.typology(edges=Array[Object])
Field Type Required Description
edges [object] yes An array of all the connections between nodes.
edges.from string yes The node's name or id where the edge start: the source node of the link.
edges.to string yes The node's name or id where the edge end: the target node of the link.
edges.weight number yes The weight of the connection: how strong is the bond among the linked nodes.
Example
// The list of edges
const edges = [
  {from: "Napoleon", to: "Myriel", weight: 1},
  {from: "Mme.Magloire", to: "Myriel", weight: 10},
  {...}
];
// Measure the influence score and detect the influence roles
const nodes = watchcomplexity.influence.typology(edges);

That will return:

{
 ranking: 
   [
      {node: "Marius", role: "Hub", score: 100},
      {node: "Courfeyrac", role: "Amplifier", score: 93.68029739776952},
      {node: "Enjolras", role: "Reducer", score: 92.93680297397769},
      {node: "Fantine", role: "Amplifier", score: 89.96282527881041},
      ...,
      {node: "Mme.Hucheloup", role: "Emitter", score: 39.405204460966544},
      {node: "Anzelma", role: "Low emitter", score: 34.94423791821562},
      {node: "Pontmercy", role: "Reducer", score: 33.08550185873605},
      ...
      {node: "OldMan", role: "Emitter branch", score: 1.4869888475836461},
      {node: "Napoleon", role: "Emitter branch", score: 0}
   ],
  nodes: 77,
  edges: 254,
  distribution:  {
    Blackhole: 0,
    Vulcano: 0,
    Channeler: 0,
    Chain: 0,
    Bridge: 1,
    Connector: 3,
    'Emitter branch': 15,
    'Receiver branch': 2,
    Receiver: 2,
    Emitter: 4,
    'Low emitter': 9,
    Idle: 0,
    Transceiver: 5,
    Tophub: 0,
    Hub: 1,
    Dam: 6,
    Reducer: 12,
    Megamplifier: 7,
    Amplifier: 10
  }
}
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