TASK Quarterly

Optimizing Graph Learning using Hierarchical Graph Adjacency Matrix (HGAM)

Wed, 19 Nov 2025 00:00:00 +0100

Graph Neural Networks (GNNs) have been increasingly adopted in modern, large-scale applications, such as social network analysis, recommendation systems, drug discovery, and more. However, the training cost of GNNs can be computationally prohibitive, especially when the graph is large and complex, necessitating the use of a mini-batching approach. In this paper, we propose a novel data structure called the Hierarchical Graph Adjacency Matrix (HGAM) to accelerate GNN training by avoiding redundant computations. With HGAM, we can accelerate the training speed of GNNs by up to four times. Additionally, we propose optimizations on top of HGAM to further enhance performance, achieving an overall speedup of up to 7.72 times for training 3-layer deep GNNs. We evaluated our techniques using three benchmark datasets—Reddit, ogbn-products, and ogbn-mag—and demonstrate that the proposed HGAM technique and related optimizations are advantageous for GNN training across modern hardware platforms.

Time reversal symmetry in noncommutative phase space of the canonical type

Volodymyr Tkachuk, Khrystyna Gnatenko — Wed, 19 Nov 2025 00:00:00 +0100

An algebra with noncommutativity of coordinates and noncommutativity of momenta which describes a rotationally- and time-reversal invariant quantum space is considered. A particle in a uniform field is studied in the space and the effect of space quantization on the energy levels of the particle is examined.
We find a perihelion shift of a particle in a Coulomb potential in noncommutative phase space. Upper bounds for the parameters of noncommutativity are estimated

Heparin Inclusion in Cyclodextrins: A Combined MD and ITC Study

Sergey Samsonov, Nathalia Pavan, Dariusz Wyrzykowski, Ola Grabowska — Wed, 19 Nov 2025 00:00:00 +0100

Cyclodextrins (CDs) are cyclic oligosaccharides widely used as host molecules capable of forming inclusion complexes
with a variety of guest compounds. In this study, for the first time, we investigated the potential interactions between
α-, β -, and γ-CD and heparin (HP), a highly sulfated glycosaminoglycan, using a combined theoretical and experimen-
tal approach. All-atom molecular dynamics (MD) simulations showed that HP does not enter the CD cavity, instead
remaining adsorbed on the external surface at the secondary cavity of the CD. Free energy calculations using LIE
and MM-GBSA confirmed that complex formation is enthalpically unfavorable in water, with desolvation penalties
outweighing van der Waals and electrostatic contributions. The binding observed in the MD simulations could be,
therefore, driven by the entropy. Potential of mean force (PMF) analysis further demonstrated that HP translocation
through the γ-CD cavity requires overcoming a high energy barrier of ≈ 23 kcal/mol, indicating that the inclusion is
not spontaneous. Complementary isothermal titration calorimetry (ITC) measurements in aqueous buffer also showed
negligible enthalpy changes, suggesting that complex formation is not driven by detectable heat effects and is predomi-
nantly entropically controlled. Overall, our findings highlight the limitations of natural cyclodextrins in encapsulating
large, highly charged polysaccharides such as HP, emphasizing the need for their chemical modifications to enable
effective host–guest complexation in such systems potentially designed to encapsulate this cargo