Graph embedding techniques have gained increasing attention for their ability to encode the complex structural information of networks into low-dimensional vectors. Existing graph embedding methods have achieved considerable success in various applications. However, these methods have limitations in capturing global graph topology information and fail to provide insights into the underlying mechanisms of network function. In this paper, we propose IsoGloVe, a count-based method that encodes graph topology into vectors using the co-occurrence statistics of fixed-size routes in random walks. IsoGloVe calculates the final embeddings based on the geodesic distances of the node’s neighbors on a manifold. This representation in geodesic space allows for the analysis of node interactions and contributes to a better understanding of complex network structure and function. The performance of IsoGloVe is evaluated on various protein-protein interactions (PPI) using graph reconstruction, node classification, and visualization. The findings reveal that IsoGloVe surpasses other comparable methods with a 30% increase in MAP for graph reconstruction and a 25% increase in model scores for node classification in the Yeast PPI network. In addition, IsoGloVe demonstrated a 6.9% increase in MAP for graph reconstruction on the Human PPI network.
Article ID: 2023S2
Publisher: Canadian Artificial Intelligence Association