Functional-Edged Network Modeling
This work addresses network analysis for domains like transportation systems where edges have functional characteristics, representing an incremental advancement in functional data methods for networks.
The paper tackles network modeling where edges represent functional data rather than simple relationships, transforming adjacency matrices into functional adjacency tensors and using Tucker decomposition with symmetry regularization for node communities. It demonstrates the model's effectiveness through simulation data and real metro system data from Hong Kong and Singapore, showing improved performance in handling irregular observations.
Contrasts with existing works which all consider nodes as functions and use edges to represent the relationships between different functions. We target at network modeling whose edges are functional data and transform the adjacency matrix into a functional adjacency tensor, introducing an additional dimension dedicated to function representation. Tucker functional decomposition is used for the functional adjacency tensor, and to further consider the community between nodes, we regularize the basis matrices to be symmetrical. Furthermore, to deal with irregular observations of the functional edges, we conduct model inference to solve a tensor completion problem. It is optimized by a Riemann conjugate gradient descent method. Besides these, we also derive several theorems to show the desirable properties of the functional edged network model. Finally, we evaluate the efficacy of our proposed model using simulation data and real metro system data from Hong Kong and Singapore.