The link prediction extension is based on Gephi uses all its advantages. Gephi is the open source leading visualization and exploration software for all kinds of graphs and networks.
In summary, an extension of Link Prediction was developed within a tool for managing graphs, representing social networks.
Activity flow
Then the type of graph loaded must be recognized, so Gephi does that job for us.
The catalog of metrics developed was added within the statistics panel. There are more than 15 different metrics to select, and all calculate the similarity between two users differently.
In addition, it is possible to cancel the task at runtime and also access old results easily. Depending on the chosen metric, the corresponding parameters must be configured in the application. For example, a capture of the configuration screen is included
Type of graphs
To carry out the technique of Link Prediction is necessary to classify the type of graph according to different factors.
- Time stamps on edges
- Dynamic graph
- Static graph
- Edge type
- Directed graph
- Undirected graph
- Mixed graph
- For additional information
- Nodes with addittional attributes
- Nodes without additional attributes
Choice of metrics
In addition, it is possible to cancel the task at runtime and also access old results easily. Depending on the chosen metric, the corresponding parameters must be configured in the application. For example, a capture of the configuration screen is included
In dynamic graphs, the user must define the initial time, called T0, to take that moment as the basis, and then compare the links that were generated dynamically with respect to the predictions. On the other hand, in static graphs, it is necessary to simulate the previous behavior. The technique hides random links from the graph, makes predictions, and then compares them to hidden links.
Number of predictions → Number of future relationships to be predicted by each user
Generation and evaluation of predictions
The three fundamental steps to carry out the link prediction technique for each user are:
1- Links are predicted according to the configured parameters.
2- Those who have the greatest similarity as predictions.
3- It is verified that the predictions performed correspond to the hidden links.
Visualization of results
- Precision: # Correct Predictions / # Predictions
- Recall: # Correct Predictions / # Hidden Links
- Roc Curve: Compares correct predictions with respect to incorrect predictions as values of similarity vary
- Area under the curve (AUC)
Additional Functions
Clustering
- Calculate similarity for each edge of the initial graph
- Sort decreasing similarity ranking
- Removes percentage of irrelevant links
Metrics on a graph
- Do not calculate predictions
- Add column for selected metric in edge table
- Assigns similarity between each pair of existing users
- Possibility to export statistics within data table
Metric combination
- It tries to improve the performance of individual predictor
- Combine those metrics with the same output type
- Maintains flexibility in parameter settings
- Similarity = Average of individual similarities
How can I install this tool on Gephi ?
ResponderEliminarYou should run executable under gephi-master\modules\application\target\gephi\bin\
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