The spaceborne gravitational wave (GW) detectors LISA and TAIJI are planned to be launched in the 2030s. The dual detectors with comparable sensitivities will form a network observing GWs with significant advantages. In this work, we investigate the three possible LISA-TAIJI networks for the different location and orientation compositions of the LISA orbit ($+60°$ inclination and trailing the Earth by 20°) and alternative TAIJI orbit configurations, including TAIJIp ($+60°$ inclination and leading the Earth by 20°), TAIJIc ($+60°$ inclination and colocated with LISA), and TAIJIm ($-60°$ inclination and leading the Earth by 20°). Of the three LISA-TAIJI configurations, the LISA-TAIJIm network shows the best performance in the sky localization and polarization determination for massive binary systems due to its better complementary antenna pattern, and LISA-TAIJIc could achieve the best cross-correlation and observe the stochastic GW background with optimal sensitivity.

LISA will detect gravitational waves (GWs) in the milli-Hz frequency band in space. Time-delay interferometry (TDI) is developed to suppress laser frequency noise beneath the acceleration noise and optical metrology noise. To identify stochastic GW signals, we need to characterize these noise components entangled in TDI data streams. In this work, we investigate noises characterization by combining the first-generation TDI channels from Michelson and Relay configurations. The Michelson channels are helpful to characterize acceleration noises in the lower frequency band, and the Relay configuration could effectively resolve optical path noises in the higher frequencies. Synergy could be achieved from their combination to determine these instrumental noises. Based on the characterized noises, we further reconstruct the power spectrum of noise in the selected TDI channel. Two cases are performed to characterize the spectrum shape of a stochastic GW signal. For a modeled signal, its parameter(s) could be directly estimated from the TDI data, and its spectrum could be recovered from the inferred values. And for an unexpected signal, its spectrum may be recognized and retrieved from noise-subtracted residual in which its power spectral density surpasses the noise level.