More Like this

1. MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

   https://doi.org/10.24963/ijcai.2019/489  

   Sun, Yiwei; Wang, Suhang; Hsieh, Tsung-Yu; Tang, Xianfeng; Honavar, Vasant (August 2019, Twenty-Eighth International Joint Conference on Artificial Intelligence)

   Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks. 

   more » « less
2. MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

   Sun, Yiwei; Wang, Suhang; Hsieh, Tsung-Yu; Tang, Xianfeng; Honavar, Vasant G. (January 2019, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence)

   Data from many real-world applications can be nat- urally represented by multi-view networks where the different views encode different types of rela- tionships (e.g., friendship, shared interests in mu- sic, etc.) between real-world individuals or enti- ties. There is an urgent need for methods to ob- tain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi- view learning focuses on data that lack a net- work structure, and most of the work on net- work embeddings has focused primarily on single- view networks. Against this background, we con- sider the multi-view network representation learn- ing problem, i.e., the problem of constructing low- dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the informa- tion from the individual network views, while ac- counting for connectivity across (and hence com- plementarity of and correlations between) differ- ent views. The results of our experiments on two real-world multi-view data sets show that the em- beddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks. 

   more » « less
3. MoReL: Multi-omics Relational Learning

   Hasanzadeh, Arman; Hajiramezanali, Ehsan; Duffield, Nick; Qian, Xiaoning (April 2022, The Tenth International Conference on Learning Representations)

   Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may manifest heterogeneous structures and data quality as often existing data may be collected from different subjects under different conditions for each type of omics data. We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across such heterogeneous views, using a fused Gromov-Wasserstein (FGW) regularization between latent representations of corresponding views for integrative analysis. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, either with graph-structured or unstructured data in different views, but also increases the model flexibility with the distribution-based regularization. This allows efficient alignment of heterogeneous latent variable distributions to derive reliable interaction predictions compared to the existing point-based graph embedding methods. Our experiments on several real-world datasets demonstrate enhanced performance of MoReL in inferring meaningful interactions compared to existing baselines. 

   more » « less
4. Morel: Multi-omics relational learning

   Hasanzadeh, Arman (January 2022, ICLR 2022)

   Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may manifest heterogeneous structures and data quality as often existing data may be collected from different subjects under different conditions for each type of omics data. We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across such heterogeneous views, using a fused Gromov-Wasserstein (FGW) regularization between latent representations of corresponding views for integrative analysis. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, either with graph-structured or unstructured data in different views, but also increases the model flexibility with the distribution-based regularization. This allows efficient alignment of heterogeneous latent variable distributions to derive reliable interaction predictions compared to the existing point-based graph embedding methods. Our experiments on several real-world datasets demonstrate enhanced performance of MoReL in inferring meaningful interactions compared to existing baselines. 

   more » « less
5. Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data

   https://doi.org/10.1371/journal.pcbi.1011044  

   Zhang, Lixiang; Lin, Lin; Li, Jia (April 2023, PLOS Computational Biology)

   Alber, Mark (Ed.)

   Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis , merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods. 

   more » « less