Jingying Zhang | Searcharxiv

arXiv2026

Strain-controlled sign reversal of the anomalous Hall effect in Ru/[Co/Ni]$_N$ multilayers

The anomalous Hall effect (AHE) is a hallmark transport phenomenon in ferromagnets arising from relativistic spin-orbit interaction. Here, we report an unexpected sign reversal of the AHE in Ru/[Co/Ni]$_N$ multilayers controlled by the stacking sequence of the Ru layer. When Ru is placed beneath, rather than atop, the Co/Ni multilayers, the anomalous Hall signal switches from positive to negative. By systematically varying the multilayer repeat number N and combining transport measurements with first-principles calculations, we show that this reversal originates from in-plane tensile strain imposed by the Ru underlayer, which reshapes the electronic structure and redistributes Berry curvature near the Fermi level. Our findings establish interfacial strain as an effective knob for tuning Berry-curvature-driven transport and suggest a pathway toward strain-controlled topological transport phenomena in magnetic multilayers.

cond-mat.mtrl-sci↗

arXiv2015

On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21X speedup in execution time while maintaining matching performance in terms of influence spread.

cs.SI↗