Clustering signifiers in a semantics graph
First Claim
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1. A method comprising:
- generating a semantics graph that represents content extracted from an enterprise network in the form of signifiers, wherein nodes of the semantics graph represent the signifiers;
coarsening the semantics graph of the signifiers into a number of sub-graphs, the number of sub-graphs including a particular sub-graph that comprises a first node and a second node of the semantics graph;
after coarsening the semantics graph into the number of sub-graphs, splitting the particular sub-graph into multiple clusters comprising a first cluster that includes the first node and a second cluster that includes the second node; and
after splitting the particular sub-graph into the multiple clusters, reducing an edge-cut of the multiple clusters by switching the first node from the first cluster to the second cluster and switching the second node from the second cluster to the first cluster.
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Abstract
Clustering signifiers in a semantics graph can comprise coarsening a semantics graph associated with an enterprise communication network containing a plurality of nodes into a number of sub-graphs containing supernodes; partitioning each of the number of sub-graphs into a number of clusters; and iteratively refining the number of clusters to reduce an edge-cut of the semantics graph, based on the number of clusters.
26 Citations
20 Claims
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1. A method comprising:
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generating a semantics graph that represents content extracted from an enterprise network in the form of signifiers, wherein nodes of the semantics graph represent the signifiers; coarsening the semantics graph of the signifiers into a number of sub-graphs, the number of sub-graphs including a particular sub-graph that comprises a first node and a second node of the semantics graph; after coarsening the semantics graph into the number of sub-graphs, splitting the particular sub-graph into multiple clusters comprising a first cluster that includes the first node and a second cluster that includes the second node; and after splitting the particular sub-graph into the multiple clusters, reducing an edge-cut of the multiple clusters by switching the first node from the first cluster to the second cluster and switching the second node from the second cluster to the first cluster. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A non-transitory computer-readable medium storing instructions executable by a processing resource to:
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generate a weighted semantics graph that represents content extracted from an enterprise network in the form of signifiers, wherein nodes of the weighted semantics graph represent the signifiers; coarsen the weighted semantics graph into a number of sub-graphs, including a particular sub-graph comprising a supernode comprising multiple individual nodes of the semantics graph; after coarsening of the semantics graph, split the particular sub-graph into a first cluster and a second cluster of equal weight; and after splitting the particular sub-graph into the first and second clusters, reduce an edge-cut of the first and second clusters by switching an individual node in the first cluster with another individual node in the second cluster. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A system comprising:
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a processing resource; and a memory resource coupled to the processing resource, the memory resource comprising instructions executable by the processing resource to; generate a semantics graph that represents content extracted from an enterprise network in the form of signifiers, wherein nodes of the semantics graph represent the signifiers; create a coarsened graph from a semantics graph, the coarsened graph comprising multiple sub-graphs, the multiple sub-graphs comprising a particular sub-graph including a first node and a second node of the semantics graph; create a partitioned graph from the coarsened graph by splitting each of the multiple sub-graphs into multiple clusters, including splitting the particular sub-graph itself into multiple clusters comprising a first cluster including the first node and a second cluster including the second node; and reduce an edge-cut of the partitioned graph using local refinement heuristics. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification