Node embedding learns low-dimensional vectors for nodes in the graph. Recent
state-of-the-art embedding approaches take Personalized PageRank (PPR) as the
proximity measure and factorize the PPR matrix or its adaptation to generate
embeddings. However, little previous work analyzes what information is encoded
by these approaches, and how the information correlates with their superb
performance in downstream tasks. In this work, we first show that
state-of-the-art embedding approaches that factorize a PPR-related matrix can
be unified into a closed-form framework. Then, we study whether the embeddings
generated by this strategy can be inverted to better recover the graph topology
information than random-walk based embeddings. To achieve this, we propose two
methods for recovering graph topology via PPR-based embeddings, including the
analytical method and the optimization method. Extensive experimental results
demonstrate that the embeddings generated by factorizing a PPR-related matrix
maintain more topological information, such as common edges and community
structures, than that generated by random walks, paving a new way to
systematically comprehend why PPR-based node embedding approaches outperform
random walk-based alternatives in various downstream tasks. To the best of our
knowledge, this is the first work that focuses on the interpretability of
PPR-based node embedding approaches.

该研究论文讨论了节点嵌入、个性化 PageRank、图拓扑以及基于 PPR 的嵌入方法对各种下游任务的优势的解释性分析。