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MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
[article]
2020
pre-print
Preserved Fulltext
Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? ...
In this paper, we develop an end-to-end model MultiImport, which infers latent node importance from multiple, potentially overlapping, input signals. ...
We formulate the problem of inferring node importance in a KG from multiple input signals. • Algorithm. ...
doi:10.1145/3394486.3403093
arXiv:2006.12001v1
fatcat:tcmlgpa57vdltjfda4bmt3b4ee
J-Recs: Principled and Scalable Recommendation Justification
[article]
2020
arXiv
pre-print
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The challenge of jointly processing multiple types of data is addressed by designing a principled graph-based approach for justification generation. ...
Existing post-hoc methods are often limited in providing diverse justifications, as they either use only one of many available types of input data, or rely on the predefined templates. ...
GENI [38] and MultiImport [39] are semi-supervised techniques to estimate node importance by considering both the graph structure and real-world signals of node popularity. ...
arXiv:2011.05928v1
fatcat:3dqm6aqazzbc5fcmwiapv7ewza
Mining and Learning With Graphs and Tensors
2022
Preserved Fulltext
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We first propose novel graph-regularized semi-supervised algorithms for estimating node importance in a knowledge graph, which achieve up to 25% higher accuracy than the best baseline.Then we develop distributed ...
In addition, we develop a method that explains product recommendations, up to 21% more accurately than the best baseline, by performing personalized inference over a product graph. ...
Chapter 3 Inferring Node Importance in a Knowledge Graph from Multiple Input Signals 2 Chapter based on work published in KDD 2020 [PKD + 20].
Definition 3. ...
doi:10.1184/r1/19891765.v1
fatcat:w47xj5l3snehjkrdmp77yqphve