arXiv subjects

Zhixing He

Publications and source records attributed to Zhixing He.

3 recordsLinked to original sources

arXiv2026

Approximate Butterfly Counting in Sublinear Time

Bipartite graphs serve as a natural model for representing relationships between two different types of entities. When analyzing bipartite graphs, butterfly counting is a fundamental research problem that aims to count the number of butterflies (i.e., 2x2 bicliques) in a given bipartite graph. While this problem has been extensively studied in the literature, existing algorithms usually necessitate access to a large portion of the entire graph, presenting challenges in real scenarios where graphs are extremely large and I/O costs are expensive. In this paper, we study the butterfly counting problem under the query model, where the following query operations are permitted: degree query, neighbor query, and vertex-pair query. We propose TLS, a practical two-level sampling algorithm that can estimate the butterfly count accurately while accessing only a limited graph structure, achieving significantly lower query costs under the standard query model. TLS also incorporates several key techniques to control the variance, including "small-degree-first sampling" and "wedge sampling via small subsets". To ensure theoretical guarantees, we further introduce two novel techniques: "heavy-light partition" and "guess-and-prove", integrated into TLS. With these techniques, we prove that the algorithm can achieve a (1+eps) accuracy for any given approximation parameter 0 < eps < 1 on general bipartite graphs with a promised time and query complexity. In particular, the promised time is sublinear when the input graph is dense enough. Extensive experiments on 15 datasets demonstrate that TLS delivers robust estimates with up to three orders of magnitude lower query costs and runtime compared to existing solutions.

cs.DB↗

arXiv2021

AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data

Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giving precise model predictions. An automatic machine learning framework is needed to ease the manual efforts in fine-tuning the models so that the experts can focus more on other problems that really need humans' engagement such as problem definition, deployment, and business services. However, there are three main challenges for building automatic solutions for temporal relational data: 1) how to effectively and automatically mining useful information from the multiple tables and the relations from them? 2) how to be self-adjustable to control the time and memory consumption within a certain budget? and 3) how to give generic solutions to a wide range of tasks? In this work, we propose our solution that successfully addresses the above issues in an end-to-end automatic way. The proposed framework, AutoSmart, is the winning solution to the KDD Cup 2019 of the AutoML Track, which is one of the largest AutoML competition to date (860 teams with around 4,955 submissions). The framework includes automatic data processing, table merging, feature engineering, and model tuning, with a time\&memory controller for efficiently and automatically formulating the models. The proposed framework outperforms the baseline solution significantly on several datasets in various domains.

cs.LG↗

arXiv2020

Assembly and disorder dissipation in superparamagnetic nanoparticle chains in a rotating magnetic field

We investigate the formation of chains of superparamagnetic iron oxide nanoparticles (SPIONs) in a rotating magnetic field, combining two well-explored chain-forming systems: larger micron-scale beads in a rotating magnetic field, and SPIONs in a static field. This simple combination is interesting because it features self-assembly that occurs both far from equilibrium and at a finite temperature, with the better-explored systems constituting respectively its zero temperature and near-equilibrium limits. Theories applicable to either of the two limits qualitatively predict the chain length distributions, except that chains in our experiments are shorter, which we attribute to the simultaneous presence of thermal fluctuations and fluid shear forces that work in concert to break chains apart. Our most striking result is that the disorder in the SPION chains gradually dissipates over a timescale of tens of minutes, about two orders of magnitude slower than the characteristic chain assembly time. The disorder dissipation can be sped up by increasing particle concentration and solution ionic strength, both of which increase the speed of chain assembly. This strongly suggests that the improvement in chain order with time is not due to thermal fluctuations but rather to energy imparted by the self-assembly process, which continually causes chains to grow and break apart, even when a steady state distribution has obtained. More generally, our results indicate that self-assembly away from equilibrium may sometimes lead to better ordered assemblies than under near-equilibrium conditions.

cond-mat.mes-hall↗