Publications

2024

1. 

   Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective

   Parsa Haddadian, Roya Booryaee, Rooholah Abedian, and 1 more author

   In 2024 Third International Conference on Distributed Computing and High Performance Computing (DCHPC), Tehran, Iran, 2024

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   Over the past ten years, graph representation learning has garnered a lot of attention due to the variety of graph-structured data and its efficiency in both time and space. One essential method for obtaining effective graph representations is graph pooling. Numerous studies on the graph pooling technique have been conducted. Cutting-edge results on a range of graph representation learning tasks were made possible by the combination of graph neural networks and self-attention mechanisms. Nevertheless, the attention mechanism has limitations since it ignores nodes that have no direct connection via an edge but provide valuable network context information. This paper proposes a graph pooling approach based on Personalized PageRank and self-attention, which improves the model to take into account both node properties and graph structure. The experimental findings indicate that, with a suitable number of parameters, the MAGPool approach delivers greater accuracy on the benchmark datasets.

2023

1. 

   HiCAP: Hierarchical Clustering-based Attention Pooling for Graph Representation Learning

   Parsa Haddadian, Rooholah Abedian, and Ali Moeini

   In 2023 13th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, 2023

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   Graph representation learning has gained increasing importance in the analysis of complex relational data. The utilization of Graph Neural Networks (GNNs) has demonstrated significant potential for capturing graph structures and node features in this domain. However, the challenge of graph pooling, which involves summarizing graphs into concise representations, remains a persistent obstacle. In this study, a novel method called HiCAP (Hierarchical Cluster-Based Attention Pooling) is proposed to address the limitations of existing graph pooling approaches. By combining the strengths of node clustering and node dropping techniques, HiCAP establishes a hierarchical framework. Initially, a soft cluster assignment matrix is learned through the application of a GNN. Subsequently, the matrix undergoes a transformation into a hard assignment by incorporating structural considerations. Attention-based scoring is subsequently employed to select representative nodes within each cluster. Extensive experimental evaluations on benchmark datasets showcase the effectiveness of HiCAP in graph classification tasks, outperforming state-of-the-art baselines. This method introduces an innovative approach to graph representation learning, thereby advancing graph pooling techniques.