Graph neural networks for integrated information and major complex estimation
Tadaaki Hosaka
TL;DR
This paper explores using graph neural networks to estimate integrated information and major complexes in large systems, enabling practical analysis of complex structures.
Contribution
A GNN model with transformer convolutions is proposed for estimating integrated information and major complexes in large systems.
Findings
The GNN model preserves qualitative patterns of integrated information in larger systems.
Local integration occurs in weakly connected subsystems, while global integration emerges with higher connectivity.
The model enables analysis of a 100-node split-brain-like system with meaningful integration patterns.
Abstract
This study investigates the potential of graph neural networks (GNNs) for estimating system-level integrated information and major complex in integrated information theory (IIT) 3.0. Owing to the hierarchical complexity of IIT 3.0, calculating the integrated information and identifying the major complex are computationally prohibitive for large systems. To overcome this difficulty, we propose a GNN model with transformer convolutions characterized by multi-head attention mechanisms for estimating the major complex and its integrated information. For evaluation, we begin by obtaining exact solutions for integrated information and major complexes in systems with 5, 6, and 7 nodes, and conduct two experiments: (1) a non-extrapolative setting in which the model is trained and tested on a mixture of systems with 5, 6, and 7 nodes, and (2) an extrapolative setting in which systems with 5 and…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Functional Brain Connectivity Studies · Big Data and Digital Economy