Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block
Model
- URL: http://arxiv.org/abs/2209.13762v1
- Date: Wed, 28 Sep 2022 01:19:38 GMT
- Title: Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block
Model
- Authors: Tianxi Cai, Dong Xia, Luwan Zhang and Doudou Zhou
- Abstract summary: Network analysis has been a powerful tool to unveil relationships and interactions among a large number of objects.
Yet its effectiveness in accurately identifying important node-node interactions is challenged by the rapidly growing network size.
This paper develops a unified framework of simultaneous grouping and connectivity analysis by combining multiple data sources.
- Score: 12.234494052824921
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Network analysis has been a powerful tool to unveil relationships and
interactions among a large number of objects. Yet its effectiveness in
accurately identifying important node-node interactions is challenged by the
rapidly growing network size, with data being collected at an unprecedented
granularity and scale. Common wisdom to overcome such high dimensionality is
collapsing nodes into smaller groups and conducting connectivity analysis on
the group level. Dividing efforts into two phases inevitably opens a gap in
consistency and drives down efficiency. Consensus learning emerges as a new
normal for common knowledge discovery with multiple data sources available. To
this end, this paper features developing a unified framework of simultaneous
grouping and connectivity analysis by combining multiple data sources. The
algorithm also guarantees a statistically optimal estimator.
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