Feature vector samplers

In order to select the possible target-candidate user pairs for generating feature vectors for supervised methods, the RELISON library provides several methods. We summarize them below:

All
Distance two
Distance two link prediction
Link prediction
Recommender

All

This sampler selects all the possible target-candidate user pairs (it just removes pairs in the training test).

Configuration file

All:

Distance two

This sampler just selects all the candidate users who share at least a common neighbor with the target user.

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))

Configuration file

Distance two:
  uSel:
    type: orientation
    value:  IN/OUT/UND/MUTUAL
  vSel:
    type: orientation
    value: IN/OUT/UND/MUTUAL

Distance two link prediction

This sampler just selects candidate users who share at least a common neighbor with the target user. It selects all the target-candidate user pairs in that collection, along with an equal number of them.

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))

Configuration file

Distance two link prediction:
  uSel:
    type: orientation
    value:  IN/OUT/UND/MUTUAL
  vSel:
    type: orientation
    value: IN/OUT/UND/MUTUAL

Link prediction

This sampler just selects all the target-candidate user pairs in the test set, along with an equal number of negative links (not in the training set).

Configuration file

Link prediction:

Recommender

For each target user, it takes the top \(k\) recommended people as the sampled individuals.

Parameters

k: the maximum number of target-candidate user pairs to retrieve for each target user.
rec: the recommendation algorithm.

Configuration file:

Recommender:
  k:
    type: int
    value: 1000
  rec:
    type: object
    object:
      name: recommender_name
      params:
        parameter_name1:
          type: parameter_type
          value: parameter_value
        parameter_name2:
        <...>

Any recommendation algorithm can be used here, so, take a look at the algorithm configuration to determine the best option.