The paper introduces an unsupervised framework that merges manifold learning with rank‑based interpretable graph embeddings to address the Geometric and Interpretability Gaps in visual representation learning. By first analyzing contextual information on the dataset manifold and then producing sparse, self‑explainable embeddings, the method achieves dimensionality reduction while preserving or improving performance in image retrieval and semi‑supervised Graph Convolutional Network classification. Experiments across varied datasets confirm that these context‑aware representations maintain high downstream effectiveness.
arXiv:2608.29001v1 Announce Type: new Abstract: In a data-driven world, efficiently organizing and mapping relationships between objects is crucial. Graphs are powerful tools for modeling these conne...
The paper introduces a new image retrieval framework that merges neighbor embedding projections with rank-based manifold learning via rank aggregation. It uses UMAP to create low‑dimensional feature representations and combines ranked lists from UMAP and rank‑based re‑ranking methods using the Borda Count strategy. Experiments on public datasets with ResNet152, Swin Transformer, and DINOv2 features show that this combined approach improves retrieval performance, especially in scenarios where baseline representations have low precision.
arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.
The paper introduces a compositional graph embedding framework based on Aitchison geometry, where nodes are represented as simplex-valued mixtures over latent archetypal factors. By embedding these mixtures using isometric log-ratio coordinates, the method preserves Aitchison distances while allowing unconstrained optimization in Euclidean space, yielding intrinsically interpretable embeddings. The approach achieves competitive performance on node classification and link prediction tasks and enables principled component restriction through subcompositional coherence, allowing analysis of how archetype groups influence representations and predictions.
arXiv:2607. 09104v1 Announce Type: cross Abstract: While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.
arXiv:2606. 17406v1 Announce Type: cross Abstract: Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes.
The paper introduces three distance‑based graph autoencoder variants that add structural penalties to the reconstruction loss. All models use a two‑layer Graph Convolutional Network encoder and a Euclidean‑distance decoder, with two node‑level regularizers: a hub penalty based on degree centrality and a penalty based on Natural Community Local Intrinsic Dimensionality (NC‑LID). Experiments on multiple dynamic graph datasets show that incorporating NC‑LID regularization consistently improves reconstruction performance compared to baselines without structural regularization and to the hub‑aware variant.
Graph-level out-of-distribution (OOD) generalization is critical for deploying graph neural networks in real-world applications where distribution shifts are inevitable. Existing approaches typically...
arXiv:2606. 03495v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge.
SCARCE is a label‑free representation‑learning framework that improves graph‑level out‑of‑distribution generalization. It uses an environment partition of training graphs—derived from metadata or self‑supervised clustering—to warm‑start a variational graph autoencoder and then applies an adversarial confusion mechanism that prevents a discriminator from distinguishing environments while preserving reconstruction fidelity. Theoretical analysis shows that this environment confusion contracts environment‑discriminative directions, effectively reweighting features to reduce reliance on spurious correlations, and experiments on synthetic and DrugOOD datasets demonstrate state‑of‑the‑art OOD performance among label‑free methods.
arXiv:2511. 04539v2 Announce Type: replace-cross Abstract: In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties covary and partially overlap across individuals and conditions.
The paper introduces the Rashomon set for dimension reduction, a collection of equally good embeddings that preserve high‑dimensional structure. It proposes PCA‑informed alignment to make axes interpretable, concept‑alignment regularization to incorporate external knowledge, and a method to extract trustworthy nearest‑neighbor relationships across the Rashomon set for refined embeddings. These techniques aim to produce interpretable, robust, and goal‑aligned visualizations by leveraging multiple valid embeddings instead of a single one.