Estimating Early Fundraising Performance of Innovations via Graph-Based Market Environment Model

Wu, Likang; Li, Zhi; Zhao, Hongke; Pan, Ziyu; Liu, Qi; Chen, Enhong

doi:10.1609/aaai.v34i04.6110

Cited by 18 publications

(5 citation statements)

References 14 publications

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“…Along with spectral graph convolution, directly performing graph convolution in the spatial domain was also investigated by many researchers [8,12]. Among them, graph attention networks [34] has aroused considerable research interest, since it adaptively specify weights to the neighbors of a node by attention mechanism [1,37].…”

Section: Related Workmentioning

confidence: 99%

Learning the Implicit Semantic Representation on Graph-Structured Data

Zhao

et al. 2021

Preprint

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Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited. In this paper, we propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs. In previous work, there are explorations of graph semantics via meta-paths. However, these methods mainly rely on explicit heterogeneous information that is hard to be obtained in a large amount of graph-structured data. SGCN first breaks through this restriction via leveraging the semantic-paths dynamically and automatically during the node aggregating process. To evaluate our idea, we conduct sufficient experiments on several standard datasets, and the empirical results show the superior performance of our model. 4

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Section: Related Workmentioning

confidence: 99%

Learning the Implicit Semantic Representation on Graph-Structured Data

Zhao

et al. 2021

Preprint

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“…However, we still lack a sufficiently deep and quantitative exploration of the potential performance before the project sets up. There is a very recent work focused on how to predict the fund-raising performances of projects in the crowdfunding market [6]. However, in [6], the LSTM-modeled states undermine the possibility of model parallelizing, and the redundant information of propagation tree would depress the market evolution modeling.…”

Section: Online Crowdfundingmentioning

confidence: 99%

“…There is a very recent work focused on how to predict the fund-raising performances of projects in the crowdfunding market [6]. However, in [6], the LSTM-modeled states undermine the possibility of model parallelizing, and the redundant information of propagation tree would depress the market evolution modeling. Here we propose the novel MLP-based competitiveness quantification with prior knowledge and hierarchical updating algorithm to address these two issues respectively.…”

Section: Online Crowdfundingmentioning

confidence: 99%

“…To conquer the challenges mentioned above, we provide a preliminary study [6] on making a prediction on the fund-raising performances of the start-up projects with a graph-based model. To deeply explore the evolution of market environments, we propose a Graph-based Market Environment (GME) model, to explore the market environment of online fund-raising platforms and predict the performance of the target project.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective

Zhao

et al. 2021

Preprint

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In the online innovation market, the fund-raising performance of the startup project is a concerning issue for creators, investors and platforms. Unfortunately, existing studies always focus on modeling the fund-raising process after the publishment of a project but the predicting of a project attraction in the market before setting up is largely unexploited. Usually, this prediction is always with great challenges to making a comprehensive understanding of both the start-up project and market environment. To that end, in this paper, we present a focused study on this important problem from a market graph perspective. Specifically, we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment. In addition, we discriminatively model the project competitiveness and market preferences by designing two graph-based neural network architectures and incorporating them into a joint optimization stage. Furthermore, to explore the information propagation problem with dynamic environment in a large-scale market graph, we extend the GME model with parallelizing competitiveness quantification and hierarchical propagation algorithm. Finally, we conduct extensive experiments on real-world data. The experimental results clearly demonstrate the effectiveness of our proposed model.

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“…Active Learning. Active learning is a popular framework to alleviate data deficiency and it has been applied in many tasks [11,19,41,43]. Active learning framework starts with a small set of labeled data and a large set of unlabeled data.…”

Section: Related Workmentioning

confidence: 99%

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

Hao

Huang

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such property labels are generally obtained by Density Functional Theory (DFT) calculation which is extremely computational costly. An effective solution is to incorporate the unlabeled molecules in a semi-supervised fashion. However, learning semi-supervised representation for large amounts of molecules is challenging, including the joint representation issue of both molecular essence and structure, the conflict between representation and property leaning. Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules. Specifically, ASGN adopts a teacher-student framework. In the teacher model, we propose a novel semi-supervised learning method to learn general representation that jointly exploits information from molecular structure and molecular distribution. Then in the student model, we target at property prediction task to deal with the learning loss conflict. At last, we proposed a novel active learning strategy in terms of molecular diversities to select informative data during the whole framework learning. We conduct extensive experiments on several public datasets. Experimental results show the remarkable performance of our ASGN framework.

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Estimating Early Fundraising Performance of Innovations via Graph-Based Market Environment Model

Cited by 18 publications

References 14 publications

Learning the Implicit Semantic Representation on Graph-Structured Data

Learning the Implicit Semantic Representation on Graph-Structured Data

Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

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