This example demonstrates the usage of the network mining plug-in based on an artificially generated social network. The network consists of 105 nodes representing people and 240 edges representing relationships between these people. Each person has different attributes such as age, gender, income, etc., which are assigned as features to the corresponding nodes.
Network Creation Workflow (01202001_networkCreation)
This workflow demonstrates the usage of the
|Node1 ID||Node2 ID|
whereas a table that contains several rows per edge could look like this:
|Edge ID||Node ID|
To get a first impression of the network the workflow uses the
Phase of Life Prediction (01202002_phaseOfLifePrediction)
This workflow shows the benefits of integrating the network mining capability into the KNIME platform. The workflow makes use of the wealth of available data mining nodes that are available within KNIME in order to predict the phase of life attributes for all persons that are missing this information.
In order to make use of the existing nodes the network features need to be extracted into a KNIME data table using the
Network Filtering (01202003_networkFiltering)
This workflow demonstrates the usage of various nodes that allow network objects to be filtered. It shows for example the filtering of leaves using the
Network Matrix Clustering (01202004_networkMatrixClustering)
This workflow demonstrates the symbiosis between the network based data structure provided by the network plug-in and the existing data mining nodes within KNIME.
In order to make use of the existing mining nodes the workflow first converts the network structure into a standard data table using the
The cluster result is used to partition the person network into distinct partitions using the
The result of this graph projection can be visualized in an external program (e.g. visone) using the
Notice: This workflow requires the Distance Matrix plug-in, which is available as a KNIME extension form the KNIME update site.
Network Looping (01202005_networkLooping)
This workflow demonstrates how subgraphs can be processed in KNIME by using the available Flow Controls.
The workflow extracts each persons direct neighborhood using the
To process each row (e.g. subgraph) separately we use the "Chunk Loop Start" node with a chunk size of one. Once the NetworkCell is converted back into a network using the