Learning to Count Isomorphisms with Graph Neural Networks

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on an input graph, the space of possible query graphs is enormous, and different parts of the input graph will be triggered to match different queries. Thus, expecting a fixed representation of the input graph to match diversely structured query graphs is unrealistic. In this paper, we propose a novel GNN called Count-GNN for subgraph isomorphism counting, to deal with the above challenges. At the edge level, given that an edge is an atomic unit of encoding graph structures, we propose an edge-centric message passing scheme, where messages on edges are propagated and aggregated based on the edge adjacency to preserve fine-grained structural information. At the graph level, we modulate the input graph representation conditioned on the query, so that the input graph can be adapted to each query individually to improve their matching. Finally, we conduct extensive experiments on a number of benchmark datasets to demonstrate the superior performance of Count-GNN.Comment: AAAI-23 main trac

Evaluation of Biogas and Solar Energy Coupling on Phase-Change Energy-Storage Heating Systems: Optimization of Supply and Demand Coordination

Biogas heating plays a crucial role in the transition to clean energy and the mitigation of agricultural pollution. To address the issue of low biogas production during winter, the implementation of a multi-energy complementary system has become essential for ensuring heating stability. To guarantee the economy, stability, and energy-saving operation of the heating system, this study proposes coupling biogas and solar energy with a phase-change energy-storage heating system. The mathematical model of the heating system was developed, taking an office building in Xilin Hot, Inner Mongolia (43.96000° N, 116.03000° E) as a case study. Additionally, the Sparrow Search Algorithm (SSA) was employed to determine equipment selection and optimize the dynamic operation strategy, considering the minimum cost and the balance between the supply and demand of the building load. The operating economy was evaluated using metrics such as payback period, load ratio, and daily rate of return. The results demonstrate that the multi-energy complementary heating system, with a balanced supply and demand, yields significant economic benefits compared to the central heating system, with a payback period of 4.15 years and a daily return rate of 32.97% under the most unfavorable working conditions. Moreover, the development of a daily optimization strategy holds practical engineering significance, and the optimal scheduling of the multi-energy complementary system, with a balance of supply and demand, is realized

Towards locality-aware meta-learning of tail node embeddings on networks

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A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning various tasks including node classification and link prediction. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes. In this survey, we embark on a comprehensive review of the literature on imbalanced learning on graphs. We begin by providing a definitive understanding of the concept and related terminologies, establishing a strong foundational understanding for readers. Following this, we propose two comprehensive taxonomies: (1) the problem taxonomy, which describes the forms of imbalance we consider, the associated tasks, and potential solutions; (2) the technique taxonomy, which details key strategies for addressing these imbalances, and aids readers in their method selection process. Finally, we suggest prospective future directions for both problems and techniques within the sphere of imbalanced learning on graphs, fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG

A Study of Wolf Pack Algorithm for Test Suite Reduction

Modern smart meter programs are iterating at an ever-increasing rate, placing higher demands on the software testing of smart meters. How to reduce the cost of software testing has become a focus of current research. The reduction of test overhead is the most intuitive way to reduce the cost of software testing. Test suite reduction is one of the necessary means to reduce test overhead. This paper proposes a smart meter test suite reduction technique based on Wolf Pack Algorithm. First, the algorithm uses the binary optimization set coverage problem to represent the test suite reduction of the smart meter program; then, the Wolf Pack Algorithm is improved by converting the positions of individual wolves into a 0/1 matrix; finally, the optimal test case subset is obtained by iteration. By simulating different smart meter programs and different size test suites, the experimental result shows that the Wolf Pack Algorithm achieves better results compared to similar algorithms in terms of the percentage of obtaining both the optimal solution and the optimal subset of test overhead

Mass change of the Antarctic ice sheet inferred from ICESat and CryoSat-2

This study examined the mass change of the Antarctic ice sheet (AIS) based on ICESat and CryoSat-2 observations. We estimated the AIS exhibited mass losses of −101±15 Gt·a−1 during the ICESat period (Sept–Nov 2003 to Sept–Oct 2009) and −186±55 Gt·a−1 during the CryoSat-2 period (Jan 2011 to Dec 2015). Mass losses occurred mainly in the sectors of the Amundsen and Bellingshausen seas. Benefitting from the 30-d subcycle of CryoSat-2, we obtained monthly estimates of mass evolution. Considerable annual variations were observed in the mass evolution sequences and the climatological monthly mass evolution. Seasonal mass evolutions in the sectors of the Bellingshausen and Amundsen seas were found most representative of the annual variation. The geographical distribution characteristics of interannual AIS mass evolution were revealed by the annual average mass evolution sequences. During Jan 2011 to Dec 2015, the ice sheets in the sectors of the Bellingshausen and Amundsen seas, and the Totten Glacier, experienced increasingly rapid areal mass loss. An area of mass gain with a moderate rate of increase was found between Dronning Maud Land and Enderby Land. Rapid mass accumulation has occurred in a limited area of the Kamb Ice Stream

Preparation and Characterization of a Lecithin Nanoemulsion as a Topical Delivery System

Purpose of this study was to establish a lecithin nanoemulsion (LNE) without any synthetic surfactant as a topical delivery vehicle and to evaluate its topical delivery potential by the following factors: particle size, morphology, viscosity, stability, skin hydration and skin penetration. Experimental results demonstrated that an increasing concentration of soybean lecithin and glycerol resulted in a smaller size LNE droplet and increasing viscosity, respectively. The droplet size of optimized LNE, with the glycerol concentration above 75% (w/w), changed from 92 (F10) to 58 nm (F14). Additionally, LNE, incorporated into o/w cream, improved the skin hydration capacity of the cream significantly with about 2.5-fold increase when the concentration of LNE reached 10%. LNE was also demonstrated to improve the penetrability of Nile red (NR) dye into the dermis layer, when an o/w cream, incorporated with NR-loaded LNE, applied on the abdominal skin of rat in vivo. Specifically, the arbitrary unit (ABU) of fluorescence in the dermis layer that had received the cream with a NR-loaded LNE was about 9.9-fold higher than the cream with a NR-loaded general emulsion (GE). These observations suggest that LNE could be used as a promising topical delivery vehicle for lipophilic compounds

Soft-bodied adaptive multimodal locomotion strategies in fluid-filled confined spaces

Soft-bodied locomotion in fluid-filled confined spaces is critical for future wireless medical robots operating inside vessels, tubes, channels, and cavities of the human body, which are filled with stagnant or flowing biological fluids. However, the active soft-bodied locomotion is challenging to achieve when the robot size is comparable with the cross-sectional dimension of these confined spaces. Here, we propose various control and performance enhancement strategies to let the sheet-shaped soft millirobots achieve multimodal locomotion, including rolling, undulatory crawling, undulatory swimming, and helical surface crawling depending on different fluid-filled confined environments. With these locomotion modes, the sheet-shaped soft robot can navigate through straight or bent gaps with varying sizes, tortuous channels, and tubes with a flowing fluid inside. Such soft robot design along with its control and performance enhancement strategies are promising to be applied in future wireless soft medical robots inside various fluid-filled tight regions of the human body