41 research outputs found
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