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[Tsinghua University, oral] Graph Shortest-path Distance Estimation at IJCNN 2024, Yokohama
![[Tsinghua University, oral] Graph Shortest-path Distance Estimation at IJCNN 2024, Yokohama](https://nicovideo.cdn.nimg.jp/thumbnails/43746835/43746835.68382850)
Abstract:
We propose an effective hybrid approach jointly leveraging local and global features for shortest-path (SP) distance estimation in domain-agnostic large-scale graphs. Previous works struggle to make estimations either from node-wise local embeddings or by compressing a global SP distance matrix, causing insufficient learning at some distance and loss of accuracy. Unlike them, we find a way to better preserve local distance on node embeddings, and then integrate them with a global process for accurate estimation at every distance. First, we propose a distance-consistent embedding method that better preserves the distance between each node and its local neighbors due to resampling node occurrence on random walks. Second, we train a feed-forward network with boosting techniques (FFN-BT) to estimate SP distance from these embeddings plus existing global features. Experimental results show that our approach averagely yields 10% improved accuracy and 20% reduced time when compared to existing methods on a broad class of graphs.
Animation of interface with graphs and data with man and woman brainstorming in office
Animation of interface with graphs and data with man and woman brainstorming in office
