# Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model

arXiv: https://arxiv.org/abs/2209.13762 · HTML: https://mlsanity.com/paper/2209.13762/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 77 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 26 of 26)

- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Consistency of spectral clustering in stochastic block models](https://mlsanity.com/paper/1312.2050/) (2013)
- [Low-rank Solutions of Linear Matrix Equations via Procrustes Flow](https://mlsanity.com/paper/1507.03566/) (2015)
- [Statistical inference on random dot product graphs: a survey](https://mlsanity.com/paper/1709.05454/) (2017)
- [Tensor SVD: Statistical and Computational Limits](https://mlsanity.com/paper/1703.02724/) (2017)
- [Inference for multiple heterogeneous networks with a common invariant subspace](https://mlsanity.com/paper/1906.10026/) (2019)
- [Achieving Optimal Misclassification Proportion in Stochastic Block Model](https://mlsanity.com/paper/1505.03772/) (2015)
- [Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data](https://mlsanity.com/paper/1804.01486/) (2018)
- [Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition](https://mlsanity.com/paper/2002.04457/) (2020)
- [On Polynomial Time Methods for Exact Low Rank Tensor Completion](https://mlsanity.com/paper/1702.06980/) (2017)
- [Spectral and matrix factorization methods for consistent community detection in multi-layer networks](https://mlsanity.com/paper/1704.07353/) (2017)
- [The Hardness of Approximation of Euclidean k-means](https://mlsanity.com/paper/1502.03316/) (2015)
- [Generalized Low-rank plus Sparse Tensor Estimation by Fast Riemannian Optimization](https://mlsanity.com/paper/2103.08895/) (2021)
- [Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries](https://mlsanity.com/paper/1711.04934/) (2017)
- [The multilayer random dot product graph](https://mlsanity.com/paper/2007.10455/) (2020)
- [dna2vec: Consistent vector representations of variable-length k-mers](https://mlsanity.com/paper/1701.06279/) (2017)
- [Latent space models for multiplex networks with shared structure](https://mlsanity.com/paper/2012.14409/) (2020)
- [Joint Embedding of Graphs](https://mlsanity.com/paper/1703.03862/) (2017)
- [Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral](https://mlsanity.com/paper/1710.10936/) (2017)
- [Spectral Clustering for Multiple Sparse Networks: I](https://mlsanity.com/paper/1805.10594/) (2018)
- [Recovering shared structure from multiple networks with unknown edge distributions](https://mlsanity.com/paper/1906.07265/) (2019)
- [Estimating a network from multiple noisy realizations](https://mlsanity.com/paper/1710.04765/) (2017)
- [Mapping Between fMRI Responses to Movies and their Natural Language Annotations](https://mlsanity.com/paper/1610.03914/) (2016)
- [Robust Estimation from Multiple Graphs under Gross Error Contamination](https://mlsanity.com/paper/1707.03487/) (2017)
- [The Multiple Random Dot Product Graph Model](https://mlsanity.com/paper/1811.12172/) (2018)
- [Control Matching via Discharge Code Sequences](https://mlsanity.com/paper/1612.01812/) (2016)

## Built on by (most cited first, 3 of 3)

- [Representation learning to advance multi-institutional studies with electronic health record data from US and France](https://mlsanity.com/paper/2502.08547/) (2025)
- [Simultaneous estimation of connectivity and dimensionality in samples of networks](https://mlsanity.com/paper/2508.12483/) (2025)
- [A Common Pipeline for Harmonizing Electronic Health Record Data for Translational Research](https://mlsanity.com/paper/2509.08553/) (2025)

## Abstract (arXiv)

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. In this paper, we propose a unified multi-view sparse low-rank block model (msLBM) framework, which enables simultaneous grouping and connectivity analysis by combining multiple data sources. The msLBM framework efficiently represents overlapping information across large scale concepts and accommodates different types of heterogeneity across sources. Both features are desirable when analyzing high dimensional electronic health record (EHR) datasets from multiple health systems. An estimating procedure based on the alternating minimization algorithm is proposed. Our theoretical results demonstrate that a consensus knowledge graph can be more accurately learned by leveraging multi-source datasets, and statistically optimal rates can be achieved under mild conditions. Applications to the real world EHR data suggest that our proposed msLBM algorithm can more reliably reveal network structure among clinical concepts by effectively combining summary level EHR data from multiple health systems.