Exploring the Confounding Factors of Academic Career Success: An Empirical Study with Deep Predictive Modeling

Understanding determinants of success in academic careers is critically important to both scholars and their employing organizations. While considerable research efforts have been made in this direction, there is still a lack of a quantitative approach to modeling the academic careers of scholars due to the massive confounding factors. To this end, in this paper, we propose to explore the determinants of academic career success through an empirical and predictive modeling perspective, with a focus on two typical academic honors, i.e., IEEE Fellow and ACM Fellow. We analyze the importance of different factors quantitatively, and obtain some insightful findings. Specifically, we analyze the co-author network and find that potential scholars work closely with influential scholars early on and more closely as they grow. Then we compare the academic performance of male and female Fellows. After comparison, we find that to be elected, females need to put in more effort than males. In addition, we also find that being a Fellow could not bring the improvements of citations and productivity growth. We hope these derived factors and findings can help scholars to improve their competitiveness and develop well in their academic careers

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in heterogeneous graphs. However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing. %insufficient mining of information. To this end, we propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. To avoid the information loss caused by the single vector node representation, we first design a sequential node representation learning mechanism to represent each node as a sequence of meta-path representations during the node message passing. Then we propose a heterogeneous representation fusion module, empowering Seq-HGNN to identify important meta-paths and aggregate their representations into a compact one. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB). Experimental results show that our proposed method outperforms state-of-the-art baselines in both accuracy and efficiency. The source code is available at https://github.com/nobrowning/SEQ_HGNN.Comment: SIGIR 202

Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction

Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential partners or competitors in advance. Although important, this problem has been rarely studied in previous research due to the challenges in modelling companies' continuously evolving preferences and capturing the semantic correlations of classification codes. To fill in this gap, we propose an event-based dynamic graph learning framework for patent application trend prediction. In particular, our method is founded on the memorable representations of both companies and patent classification codes. When a new patent is observed, the representations of the related companies and classification codes are updated according to the historical memories and the currently encoded messages. Moreover, a hierarchical message passing mechanism is provided to capture the semantic proximities of patent classification codes by updating their representations along the hierarchical taxonomy. Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives. Experiments on real-world data demonstrate the effectiveness of our approach under various experimental conditions, and also reveal the abilities of our method in learning semantics of classification codes and tracking technology developing trajectories of companies.Comment: Accepted by the TKDE journa

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

Heterogeneous graph neural networks (HGNNs) have been widely applied in heterogeneous information network tasks, while most HGNNs suffer from poor scalability or weak representation when they are applied to large-scale heterogeneous graphs. To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning. Unlike traditional heterogeneous graph neural networks, we adopt the contrastive learning mechanism to deal with the complex heterogeneity of large-scale heterogeneous graphs. We first learn relation-aware node embeddings under the network schema view. Then we propose a novel positive sample selection strategy to choose meaningful positive samples. After learning node embeddings under the positive sample graph view, we perform a cross-view contrastive learning to obtain the final node representations. Moreover, we adopt the label smoothing technique to boost the performance of RHCO. Extensive experiments on three large-scale academic heterogeneous graph datasets show that RHCO achieves best performance over the state-of-the-art models

Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction

Accurate citation count prediction of newly published papers could help editors and readers rapidly figure out the influential papers in the future. Though many approaches are proposed to predict a paper's future citation, most ignore the dynamic heterogeneous graph structure or node importance in academic networks. To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers. First, a dynamic heterogeneous network embedding module is provided to capture the dynamic evolutionary trends of the whole academic network. Then, a node importance embedding module is proposed to capture the global consistency relationship to figure out each paper's node importance. Finally, the dynamic evolutionary trend embeddings and node importance embeddings calculated above are combined to jointly predict the future citation counts of each paper, by a log-normal distribution model according to multi-faced paper node representations. Extensive experiments on two large-scale datasets demonstrate that our model significantly improves all indicators compared to the SOTA models.Comment: Accepted by CIKM'202

Motion characteristics of large arrays of modularized floating bodies with hinge connections

Hinged arrays have garnered increasing interest due to their potential to provide flexible and adaptable solutions for the various challenges faced in ocean development. The effectiveness of these arrays in engineering applications heavily depends on the motion characteristics of each individual module, rather than specific modules, such as the one with the strongest motion. However, the presence of hinge constraints results in coupled motion responses of all modules instead of independent ones. The objective of this study is to investigate the motion behavior of large arrays formed by multiple floaters hinged together, while existing literature mainly focused on two-body hinged systems. Based on the potential flow theory and Rankine source panel method, a numerical program was developed to calculate the hydrodynamic interactions and the coupled motion responses. First, a model test was conducted to validate the developed frequency-domain simulations. A good agreement was achieved. Then, the effects of hinge constraints, the number of modules, and two external constraints on the motion responses of the entire array were discussed. The results indicated that the heave motion of the array subjected to hinge constraints was significantly suppressed, but a strong pitch motion occurred in a larger wavelength range. For hinged arrays, the floaters located at the two ends were most likely to be excited with the strongest motions. Moreover, a shorter hinged array could be used to quantify the trends in the motion of arrays with more floaters. The calculation results also revealed that the motion responses of a hinged array were highly sensitive to the external constraints, e.g., mooring lines

Replacement of methane from quartz sand-bearing hydrate with carbon dioxide-in-water emulsion

The replacement of CH(4) from its hydrate in quartz sand with 90:10, 70:30, and 50:50 (W(CO2):W(H2O)) carbon dioxide-in-water (C/W) emulsions and liquid CO(2) has been performed in a cell with size of empty set 36 x 200 mm. The above emulsions were formed in a new emulsifier, in which the temperature and pressure were 285.2 K and 30 MPa, respectively, and the emulsions were stable for 7-12 h. The results of replacing showed that 13.1-27.1%, 14.1-25.5%, and 14.6-24.3% of CH(4) had been displaced from its hydrate with the above emulsions after 24-96 It of replacement, corresponding to about 1.5 times the CH(4) replaced with high-pressure liquid CO(2). The results also showed that the replacement rate of CH(4) with the above emulsions and liquid CO(2) decreased from 0.543, 0.587, 0.608, and 0.348 1/h to 0.083, 0.077, 0.069, and 0.063 1/h with the replacement time increased from 24 to 96 h. It has been indicated by this study that the use of CO(2) emulsions is advantageous compared to the use of liquid CO(2) in replacing CH(4) from its hydrate

Experimental Determination of the Equilibrium Conditions of Binary Gas Hydrates of Cyclopentane plus Oxygen, Cyclopentane plus Nitrogen, and Cyclopentane plus Hydrogen

In this work, four-phase hydrate equilibrium data for each of the systems cyclopentane (CP) + water + hydrogen, CP + water + nitrogen, and CP + water + oxygen were measured and are reported in the temperature range of 281.3-303.1 K and the pressure range of 2.27-30.40 MPa. Measurements were made using an isochoric method. Experimental data on the equilibrium conditions of the two systems nitrogen + water and hydrogen + water with CP are reported over a previously uninvestigated extended temperature span. The hydrate dissociation data for the CP + water + hydrogen system are compared with some selected experimental data from the literature, and the acceptable agreement demonstrates the reliability of the experimental method used in this work. Finally, the first quadruple phase equilibrium data in the ternary system CP + water + oxygen have been achieved