arXiv:2606. 04733v1 Announce Type: new Abstract: Understanding activities of Internet scanners is challenging; it often requires identifying relationships between sources, a task for which semantic annotations are scarce.
arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.
arXiv:2510. 16311v3 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information.
The paper introduces Inductive Correlation Clustering, a new framework that uses Graph Neural Networks to solve the Correlation Clustering problem on unseen graph instances. By learning common structural patterns and node features, the method generalizes to new graphs with minimal computational overhead, achieving inference times up to five orders of magnitude faster while maintaining an approximation ratio within about 10% of the best baseline. It also demonstrates competitive performance on standard transductive benchmarks and serves as an efficient learnable pooling layer for graph classification tasks.
The advent of foundation models have enabled a new era in zero-shot classification. Yet, key challenges persist. Despite their impressive generalization power that leverages the immense pre-training k...
The paper introduces CoCo, a conjoint framework for deep graph clustering that emphasizes compactness and consistency in node representations. CoCo uses graph convolutional filters to learn robust embeddings from both local and global views, then encodes them into low‑rank compact embeddings to reduce redundancy and noise. A consistency learning strategy further enriches node semantics, enabling knowledge transfer between perspectives and yielding superior performance over state‑of‑the‑art methods on multiple datasets.
arXiv:2601. 22107v2 Announce Type: replace Abstract: We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction.
The paper introduces a divergence-based similarity function (DSF) for multi-view contrastive learning, representing each set of augmented views as a distribution and measuring similarity via distribution divergence. DSF captures joint structure across all views, outperforming prior pairwise methods on tasks such as kNN classification, linear evaluation, transfer learning, and distribution shift. It also offers greater efficiency and eliminates the need for a temperature hyperparameter, unlike cosine similarity.
arXiv:2607. 10548v1 Announce Type: cross Abstract: Pseudo-labeling based on Optimal Transport (OT) has become an effective mechanism for enhancing short text clustering.
arXiv:2607. 27761v1 Announce Type: new Abstract: In recent years, multi-view clustering has attracted widespread research interest.
arXiv:2610. 09760v1 Announce Type: new Abstract: Transformers have in-context learning capabilities, where some known learning algorithms can be executed in the forward pass through the model.
arXiv:2608.31107v1 Announce Type: new Abstract: The advent of foundation models have enabled a new era in zero-shot classification. Yet, key challenges persist. Despite their impressive generalizatio...
arXiv:2608.21825v1 Announce Type: new Abstract: Learning adjacency matrices from node-link images is a fundamental problem for recovering structured graph information from visual observations. Existi...