IGCN: Integrative Graph Convolutional Networks for Multi-modal Data
- URL: http://arxiv.org/abs/2401.17612v2
- Date: Sun, 4 Feb 2024 16:41:47 GMT
- Title: IGCN: Integrative Graph Convolutional Networks for Multi-modal Data
- Authors: Cagri Ozdemir, Mohammad Al Olaimat, Yashu Vashishath, Serdar Bozdag
and Alzheimer's Disease Neuroimaging Initiative
- Abstract summary: We introduce a novel integrative neural network approach for multi-modal data networks, named Integrative Graph Convolutional Networks (IGCN)
IGCN learns node embeddings from multiple topologies and fuses the multiple node embeddings into a weighted form by assigning attention coefficients to the node embeddings.
Our proposed attention mechanism helps identify which types of data receive more emphasis for each sample to predict a certain class.
- Score: 2.249916681499244
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Recent advances in Graph Neural Networks (GNN) have led to a considerable
growth in graph data modeling for multi-modal data which contains various types
of nodes and edges. Although some integrative prediction solutions have been
developed recently for network-structured data, these methods have some
restrictions. For a node classification task involving multi-modal data,
certain data modalities may perform better when predicting one class, while
others might excel in predicting a different class. Thus, to obtain a better
learning representation, advanced computational methodologies are required for
the integrative analysis of multi-modal data. Moreover, existing integrative
tools lack a comprehensive and cohesive understanding of the rationale behind
their specific predictions, making them unsuitable for enhancing model
interpretability. Addressing these restrictions, we introduce a novel
integrative neural network approach for multi-modal data networks, named
Integrative Graph Convolutional Networks (IGCN). IGCN learns node embeddings
from multiple topologies and fuses the multiple node embeddings into a weighted
form by assigning attention coefficients to the node embeddings. Our proposed
attention mechanism helps identify which types of data receive more emphasis
for each sample to predict a certain class. Therefore, IGCN has the potential
to unravel previously unknown characteristics within different node
classification tasks. We benchmarked IGCN on several datasets from different
domains, including a multi-omics dataset to predict cancer subtypes and a
multi-modal clinical dataset to predict the progression of Alzheimer's disease.
Experimental results show that IGCN outperforms or is on par with the
state-of-the-art and baseline methods.
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