Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions

Signed graphs are complex systems that represent trust relationships or preferences in various domains. Learning node representations in such graphs is crucial for many mining tasks. Although real-world signed relationships can be influenced by multiple latent factors, most existing methods often oversimplify the modeling of signed relationships by relying on social theories and treating them as simplistic factors. This limits their expressiveness and their ability to capture the diverse factors that shape these relationships. In this paper, we propose DINES, a novel method for learning disentangled node representations in signed directed graphs without social assumptions. We adopt a disentangled framework that separates each embedding into distinct factors, allowing for capturing multiple latent factors. We also explore lightweight graph convolutions that focus solely on sign and direction, without depending on social theories. Additionally, we propose a decoder that effectively classifies an edge's sign by considering correlations between the factors. To further enhance disentanglement, we jointly train a self-supervised factor discriminator with our encoder and decoder. Throughout extensive experiments on real-world signed directed graphs, we show that DINES effectively learns disentangled node representations, and significantly outperforms its competitors in the sign prediction task.Comment: 26 pages, 11 figure

Measurement of Magnetic Field Properties of a 3.0 T/m Air-core HTS Quadrupole Magnet and Optimal Shape Design to Increase the Critical Current Reduced by the Incident Magnetic Field

Air-core high-temperature superconducting quadrupole magnets (AHQMs) differ from conventional iron-core quadrupole magnets, in that their iron cores are removed, and instead high-temperature superconductors (HTSs) are applied. The high operating temperature and high thermal stability of HTS magnets can improve their thermodynamic cooling efficiency. Thus, HTS magnets are more suitable than low temperature superconducting magnets for withstanding radiation and high heat loads in the hot cells of accelerators. AHQMs are advantageous because they are compact, light, and free from the hysteresis of ferromagnetic materials, due to the removal of the iron-core. To verify the feasibility of the use of AHQMs, we designed and fabricated a 3.0 T/m AHQM. The magnetic field properties of the fabricated AHQM were evaluated. Additionally, the characteristics of the air-core model and iron-core model of 9.0 T/m were compared in the scale for practical operation. In comparison with the iron-core model, AHQM significantly reduces the critical current (I[subscript C]) due to the strong magnetic field inside the coil. In this study, a method for the accurate calculation of I[subscript C] is introduced, and the calculated results are compared with measured results. Furthermore, the optimal shape design of the AHQM to increase the critical current is introduced. Keywords: air-core quadrupole magnet; critical current degradation; heavy-lon accelerator; high-temperature superconductor; iron-core quadrupole magnet; optimum shape designKorea Electric Power Corporation (Grant R17XA05_32)“Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry and Energy, Republic of Korea. (No. 20184030202270