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…_tail_merging: bool) The relation_tail_merging mode applied the relation-tail merging method, as introduces in our new paper.
…is very useful for exploring and debugging our processed graph)
…skip_literals: bool).
If True, literals (=node properties/attributes) are skipped during the conversion.
Otherwise, they are converted to nodes. so that a node (n: {name: 'John'}) becomes (n)-->(name)-->(john).
…rate_candidates(): during walk candidate collection, only even walking depths (2, 4, 6...) where collected. But if we process an relation-tail merged graph, we also need to consider odd walking depths!
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Short update: Just a friendly reminder to process this pull request. I think the made changes of this branch are save to merge, because i made some tests with relation-tail merging (rtm) and without.
As we can see, MUTAG seems to benefit from rtm, the other datasets not that much... |
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Hi,
I added a new method to convert a multi-relational rdf-graph file into a non-relational graph of type
datastructures::Graph, called "relation-tail merging".It can be used by calling the
datastructures::Graph::rdflib_to_graph()method with the new input-parameterrelation_tail_merging=True.The process of relation-tail merging tries to solve the issue that predicate-nodes often carry little to no information (which is why these candidates are usually skipped during walk mining).
Relation_tail_merging works as follows (is also explained in our corresponding paper, which is currently under review):
First, the next tail node is selected, t, as well as a set of nr relations of identical type, r, where the topological form ()-r->(t) is given. The process of relation-tail merging then involves inserting a new node, rt, so that ()-r->(t) turns into (*)-->(rt)-->(t). The new directional edges, -->, are now typeless, and the new inserted node, rt, represents a "relation-modified" node and is named accordingly in the form <type_of_r>_<name_of_t>.
To implement this method, i changed the
datastructures::Graph::rdflib_to_graph()method accordingly and i also applied a small change to the functionMINDWALCMixin::_generate_candidates(), so that, if we use a relation-tail merged graph, MINDWALC does also collect walks with odd depths, so that also (rt) nodes are considered during candidate collection.I also added a mew function to the datastructures::Graph class, called graph_to_neo4j(). This method can load a given datastructures::Graph instance into a running and empty neo4j database. This can be useful to investigate our graph using the neo4j desktop app.