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HpGraphNEI: A network entity identification model based on heterophilous graph learning
N Li, T Li, Z Ma, X Hu, S Zhang, F Liu, X Quan… - Information Processing …, 2024 - Elsevier
Network entities have important asset map**, vulnerability, and service delivery
applications. In cyberspace, where the network structure is complex and the number of …
applications. In cyberspace, where the network structure is complex and the number of …
Using deep learning to find the next unicorn: A practical synthesis
Startups often represent newly established business models associated with disruptive
innovation and high scalability. They are commonly regarded as powerful engines for …
innovation and high scalability. They are commonly regarded as powerful engines for …
Data driven analysis for startup investments for venture capitalists
J Singhal, C Rane, Y Wadalkar, M Joshi… - … for Advancement in …, 2022 - ieeexplore.ieee.org
Startups are becoming an increasingly appealing investment opportunity for Venture Capital
firms and Angel in-vestors. However, in order to make educated judgments, investors must …
firms and Angel in-vestors. However, in order to make educated judgments, investors must …
Staged link prediction in bipartite investment networks based on pseudo-edge generation
J Yu, Y Lee - Information Technology and Management, 2024 - Springer
Investors, including venture capitalists, invest in companies in multiple stages depending on
the scale and timing. A second-stage investment involves investing a large amount in a few …
the scale and timing. A second-stage investment involves investing a large amount in a few …
Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method
Z Gao, Y **ao - arxiv preprint arxiv:2408.09420, 2024 - arxiv.org
In the Venture Capital (VC) industry, predicting the success of startups is challenging due to
limited financial data and the need for subjective revenue forecasts. Previous methods …
limited financial data and the need for subjective revenue forecasts. Previous methods …
[PDF][PDF] Using deep learning to find the next unicorn: A practical synthesis on optimization target, feature selection, data split and evaluation strategy
Startups represent newly established business models associated with disruptive innovation
and high scalability, hence strongly propel the economic and social development …
and high scalability, hence strongly propel the economic and social development …
[PDF][PDF] Bring me a good one: Seeking high-potential startups using heterogeneous venture information networks
Identifying startups with the highest potential for success is a complex task, necessitating the
examination of various information sources, including firm demographics, management …
examination of various information sources, including firm demographics, management …
Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems
This paper addresses the growing application of data-driven approaches within the Private
Equity (PE) industry, particularly in sourcing investment targets (ie, companies) for Venture …
Equity (PE) industry, particularly in sourcing investment targets (ie, companies) for Venture …
Machine Learning to Predict Business Success: Theories, Features, and Models
D Gangwani - 2024 - search.proquest.com
Businesses are the driving force behind economic systems and are the lifeline of the
community as they help in the prosperity and growth of the nation. Hence it is important for …
community as they help in the prosperity and growth of the nation. Hence it is important for …
Start-Up Exits: Evidence From The Czech Republic
L Vejmělková - Ekonomska misao i praksa, 2024 - search.proquest.com
The aim of this paper is to identify the main characteristics of start-up exits in the Czech
Republic. The research gap stems from the lack of research on the start-up exits. For this …
Republic. The research gap stems from the lack of research on the start-up exits. For this …