May 1, 2012 · A first step towards the unification of statistical and structural pattern recognition has been proposed in (Fu, 1986) by augmenting nodes and edges of a graph by attributes, i.e. feature vectors. This led to the standard definition of graphs used in pattern recognition today.

5 days ago · Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification ($e.g.$, using Language Models) and structure-augmented …

Mar 21, 2025 · These modules capture attribute and long-range global structure information of the input graphs. We further extend ASP to ASP-adaptive which can flexibly generate contrastive views with adaptive aggregation mechanisms.

Mar 18, 2025 · However, real-world temporal graphs often possess rich textual information, giving rise to temporal text-attributed graphs (TTAGs). Such combination of dynamic text semantics and evolving graph structures introduces heightened complexity.

Nov 22, 2017 · To address this issue, this paper proposed a novel weighted K-means algorithm with “local” learning for attributed graph clustering, called adaptive fusion of structural and attribute information (Adapt-SA) and analyzed the convergence property of the algorithm.

Aug 1, 2009 · In this paper, we propose a novel graph clustering algorithm, SA-Cluster, based on both structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values.

Jun 18, 2024 · Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification (e.g., using Language Models) and structure-augmented …

Apr 16, 2025 · In this work, we propose BiGTex (Bidirectional Graph Text), a novel architecture that tightly integrates GNNs and LLMs through stacked Graph-Text Fusion Units. Each unit allows for mutual attention between textual and structural representations, enabling information to flow in both directions, text influencing structure and structure guiding ...

Our framework learns the attribute cor-relations in the observed network and exploits a genera-tive graph model, such as the Kronecker Product Graph Model (KPGM) [11] and Chung Lu Graph Model (CL) [2], to compute structural edge probabilities.

Sep 30, 2024 · Attributed graph clustering, leveraging both structural and attribute information, is crucial in various real-world applications. However, current approaches face challenges stemming from the sparsity of graphs and sensitivity to noise in Graph Convolutional Networks (GCNs).