Enhancing Interpretability of Graph Convolutional Networks for Multi-view Learning
Bibliographic record
Abstract
The explosion of multimedia data, collected from heterogeneous sources and represented in multiple formats, has led to the formation of comprehensive multi-view or multi-modal datasets, facilitating deeper analysis and insight. These datasets cover various physical features captured by different sensors, which makes the quality distribution of data among views uneven. In recent years, Graph Convolutional Networks (GCNs) have attracted substantial attention from the academic community, leading to their widespread adoption in a variety of application domains. However, GCNs are often considered black-box models due to their complex internal operations, thereby posing significant challenges to understanding and interpreting their decision-making processes. The lack of interpretability in GCNs undermines confidence in their predictions and makes it difficult to identify and address potential biases. To tackle these issues, we propose a generic multi-view graph convolutional network, which is applied to semi-supervised classification tasks. By maximizing subspace independence and restricting network transmission weights, we aim to find interpretability for the constructed network framework from both the spatial and transmission domains. The main contributions are summarized as follows: First, we propose a general and efficient graph learning framework for multi-view representation, which simplifies both feature fusion and downstream classification tasks. Second, we design two learning strategies, focusing on constraining the weights of forward propagation and maximizing the independence of subspaces, respectively, to effectively capture the inherent characteristics between multiple views and stably propagate label information. Finally, we develop a joint network based on the proposed framework that integrates both constrained weights and learned embeddings to emphasize the most informative features from each view. We conduct extensive experiments on eight benchmark datasets, where our proposed method consistently outperforms ten state-of-the-art approaches, demonstrating its superior effectiveness across diverse multi-view learning tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".