Feature vector generation

In order to execute the supervised people recommendation / link prediction algorithms, it is first necessary to compute the feature vectors for the different links involved in the recommendation. Here, we provide information about how these features can be computed:

java -jar RELISON.jar featuregen train-network test-network multigraph directed weighted selfloops readtypes config output rec-length (-users test/all -print true/false -reciprocal true/false -distance max -feat-data file index -comms commfile)

where

train-instance-network: a file containing a network for retrieving the feature vectors for the training set.
train-class-network: a file containing a network for retrieving the relevance of each training instance.
test-instance-network: a file containing a network for retrieving the feature vectors for the test set.
test-class-network: a file containing a network for retrieving the relevance of each test instance.
directed: true if the network to study is directed, false otherwise.
weighted-sampling: true if we use edge weights for the sampler selecting a suitable set of target-candidate user pairs.
weighted-classes: true if we use edge weights as class outputs (otherwise, binary classes).
weighted-features: true if we use edge weights when computing the features.
config: a Yaml file containing the information about a) samplers, b) metrics and algorithms for computing features (see Configuration file below).
train-sampling: a Yaml configurator containing the information about the sampling method for training target-candidate user pairs.
test-sampling: a Yaml configurator containing the sampling method for the test target-candidate user pairs.
train-network : a file containing the test social network, which is used to evaluate the effectiveness of the recommendation algorithms.
multigraph: true if the network allows multiple edges between each pair of users, false otherwise.
directed: true if the network is directed, false otherwise.
weighted: true if we want to use the weights of the links, false otherwise (weights will be binary).
selfloops: true if we allow links between a node and itself, false otherwise.
readtypes: true if we want to read the types of the edges, false otherwise.
config: a Yaml configuration file for reading the people recommendation algorithms and the evaluation metrics we want to apply (see Configuration file below).
output: a directory in which to store the structural properties.
rec-length: the maximum number of links to recommend to each user.
Optional arguments:
- -users test/all: indicates whether to generate recommendations for all the users (all) or just for those who have links in the test set (test). By default: all.
- -print true/false: indicates whether we want to print the recommendations or not. By default: true.
- -reciprocal true/false: in directed networks, this parameter indicates whether we want to recommend reciprocal edges or not. By default: false.
- -distance max directed: max indicates the maximum distance between the target and the candidate users. directed indicates whether we want to consider link orientation when computing such distance. By default, it does not limit the distance.
- -feat-data file index: in case we want to compute feature-based metrics, file specifies the location of a feature file (user-feature-value tab-separated triplets), or an index containing the features. If index is equal to true, we consider that file points to an index.
- -comms commfile: route to a file containing a community partition of the network.

Configuration file

In order to select a suitable set of metrics, the program receives, as input, a configuration file, specifying the features we want to use. These features can be either a) the score of a recommendation for the link or b) the result of an structural metric. The configuration file is as it follows:

algorithms:
  algorithm_name1:
    param_name1:
      type: int/double/boolean/string/long/orientation/object
      values: [value1,value2,...,valueN] / value
      range:
      - start: startingValue
        end: endingValue
        step: stepValue
      - start: <...>
      objects:
        name_of_the_object:
          param_name1:
            type: int/double/boolean/string/long/orientation/object
            <...>
          param_name2:
            type: int/double/boolean/string/long/orientation/object
            <...>
  algorithm_name2:
    ...
metrics:
  metric_name1:
    type: vertex/pair
      params:
        param_name1:
          type: int/double/boolean/string/long/orientation/object
          values: [value1,value2,...,valueN] / value
          range:
          - start: startingValue
            end: endingValue
            step: stepValue
          - start: <...>
          objects:
            name_of_the_object:
              param_name1:
                type: int/double/boolean/string/long/orientation/object
                <...>
              param_name2:
                type: int/double/boolean/string/long/orientation/object
                <...>
  metric_name2:
    <...>

where algorithms shows the part of the configuration file dedicated to the parameter grid of the people recommendation algorithms, whereas the metric tag shows the start of the structural metrics to use. At the moment, this program does only admit metrics working over users (they are applied over both the target and candidate users) and over pairs of users.

Finally, there are two more configuration files to consider, which apply a sampling strategy to select a suitable set of target-user candidate pairs for generating the features. These configuration files are considered as follows:

samplers:
   sampler_algorithm:
     param_name1:
       type: int/double/boolean/string/long/orientation/object
       value: value
       object:
         name_of_the_object:
           name: object_name
           params:
             object_param_name1:
               type: int/double/boolean/string/long/orientation/object
               value: value
             object_param_name2:
               type: int/double/boolean/string/long/orientation/object
               <...>
             <...>

Output files

This program produces as outcome the LETOR feature files. For more information about this format, see http://terrier.org/docs/v4.0/learning.html