[1B4-GS-2] Machine learning: Expression learning

[1B4-GS-2-05] Structural embedding learning for nodes in weighted networks

〇Shu Liu¹, Fujio Toriumi¹ (1. The Univerisity of Tokyo)

Keywords:representation learning, weighted network, structural information

Complex networks consist of elements (nodes) and interactions between these elements (links), forming a data structure where the strength of interactions is captured by link weights in weighted networks, enabling the modeling of real-world interaction complexities. With significant advancements in machine learning, attempts have been made to incorporate complex networks into machine learning for advanced inference. Particularly noteworthy is the task of node embedding, where similar nodes are mapped close to each other in vector space, preserving their characteristics while mapping them to vectors. This study proposes a method for learning embedding representations that preserve the structural features of nodes in weighted networks.
Specifically, the approach involves comparing link weights of adjacent nodes up to a certain number of hops to calculate distances between nodes at multiple scales. Subsequently, weighted multi-layer graphs are constructed based on distances at each scale. Finally, node contexts are generated through random walks, and embedding representations are generated using Skip-gram. The superior performance of this method is demonstrated by confirming the interpretability of embedding representations in toy networks and the structural reproducibility in real networks.