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Found 5 papers, 2 papers with code
Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
We provide the theoretical rationale for the refinement procedure, demonstrating how and why our proposed method can effectively identify useful hidden communities via stochastic block models, and how the refinement method leads to improved vertex embedding and better decision boundaries for subsequent vertex classification.
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
To combine the strengths of these contrasting methods, we propose a Graph RAG approach to question answering over private text corpora that scales with both the generality of user questions and the quantity of source text to be indexed.
Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies, and predicting trends.
Synergistic Graph Fusion via Encoder Embedding
In this paper, we introduce a novel approach called graph fusion embedding, designed for multi-graph embedding with shared vertex sets.
