Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
Keywords: dynamic network, network embedding, network representation, temporal random walk, dynamic graphlets
Abstract: Dynamic network embedding methods transform nodes in a dynamic network into low-dimensional vectors while preserving network characteristics, facilitating tasks such as node classification and community detection. Several embedding methods have been proposed to capture $\textit{structural proximity}$ among nodes in a network, where densely connected communities are preserved, while others have been proposed to preserve $\textit{structural equivalence}$ among nodes, capturing their structural roles regardless of their relative distance in the network. However, most existing methods that aim to preserve $\textit{both}$ network characteristics mainly focus on static networks and those designed for dynamic networks do not explicitly account for inter-snapshot structural properties. This paper proposes a novel unifying dynamic network embedding method that simultaneously preserves both structural proximity and equivalence while considering inter-snapshot structural relationships in a dynamic network. Specifically, to define structural equivalence in a dynamic network, we use temporal subgraphs, known as dynamic graphlets, to capture how a node's neighborhood structure evolves over time. We then introduce a temporal-structural random walk to flexibly sample time-respecting sequences of nodes, considering both their temporal proximity and similarity in evolving structures. The proposed method is evaluated using five real-world networks on node classification where it outperforms benchmark methods, showing its effectiveness and flexibility in capturing various aspects of a network.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 10004
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