Core Concepts
The proposed Simplified Multi-hop Attention Network (SMN) effectively identifies overlapping communities by introducing a subspace community embedding technique and a hop-wise attention mechanism to capture high-order patterns while improving model efficiency.
Abstract
The paper presents a novel approach called Simplified Multi-hop Attention Network (SMN) for efficiently processing and analyzing overlapping community structures in graphs.
Key highlights:
- Subspace Community Embedding: SMN introduces a Sparse Subspace Filter (SSF) to represent each community as a sparse embedding vector. This enables nodes to be projected into multiple subspaces simultaneously, effectively addressing the overlapping community structure.
- Hop-wise Attention Mechanism: SMN employs a hop-wise attention mechanism to control the aggregation of messages from different hops, addressing the oversmoothing issue and capturing high-order patterns.
- Efficient Preprocessing and Propagation: SMN removes the non-linear activation functions during preprocessing and uses a simplified propagation framework, significantly improving the model's training efficiency.
- Subspace Community Search Algorithms: SMN proposes two query-dependent search algorithms, Sub-Topk and Sub-CS, to identify communities while mitigating the free-rider and boundary effects.
The authors demonstrate the superior performance of SMN compared to state-of-the-art approaches, achieving 14.73% improvements in F1-Score and up to 3 orders of magnitude acceleration in model efficiency.
Stats
The paper reports the following key statistics:
SMN archives 14.73% improvements in F1-Score over state-of-the-art methods.
SMN achieves up to 3 orders of magnitude acceleration in model efficiency compared to existing approaches.
Quotes
"To the best of our knowledge, we are the first to formally define the problem of overlapping community search in deep learning."
"Existing GNN-based approaches primarily focus on disjoint community structures, while real-life communities often overlap."
"The proposed hop-wise attention mechanism enhances the flexibility and robustness of SMN by attending to broader receptive fields, capturing the unique graph structure across different real-life datasets."