A Venture Capital Recommendation Algorithm based on Heterogeneous Information Network

According to its characteristics, venture capital can be described as a typical heterogeneous information network, which includes multiple kinds of nodes and various relations. Getting hints from PathRank algorithm, this paper proposes VC-Recom, a recommendation algorithm based on heterogeneous information network, which helps investment companies find suitable startup projects. Besides, the experimental results show that the proposed algorithm can produce more effective recommendation results for investment firms compared with other methods

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 team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Specifically, we construct a Heterogeneous Venture Information Network (HVIN) using raw business data and deem the prediction a node classification task. Our model integrates theory-guided semantic meta-paths, firm demographics, sampling-based self-attention, and centrality encoding to overcome certain constraints of existing GNNs. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics. Our study also includes a comprehensive interpretation analysis to provide investors with an essential understanding for better decision-making

Bring Me a Good One: Seeking High-potential Startups using Heterogeneous Venture Information Networks

The rapid acceleration of technology and the evolving global economy have led to a significant surge in high-potential startups, presenting immense opportunities for venture capital firms and investors to support and benefit from these innovative ventures. However, identifying startups with the highest likelihood of success remains a complex task, necessitating the examination of various information sources, including firm demographics, management team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics

Investor-patent networks as mutualistic networks

Venture capital investments in startups have come to represent an important driver of technological innovation, in parallel to corporate- and government-directed efforts. Part of the future of artificial intelligence, medicine and quantum computing now depends upon a large number of venture investment decisions whose robustness against increasingly frequent crises has therefore become crucial. To shed light on this issue, and by combining large-scale financial, startup and patent datasets, we analyze the interactions between venture capitalists and technologies as an explicit bipartite patent-investor network. Our results reveal that this network is topologically mutualistic because of the prevalence of links between generalist investors, whose portfolios are technologically diversified, and general-purpose technologies, characterized by a broad spectrum of use. As a consequence, the robustness of venture-funded technological innovation against different types of crises is affected by the high nestedness and low modularity, with high connectance, associated with mutualistic networks.Comment: 16 pages with appendix, 4 figures, 2 table

Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks

Building Winners? An Empirical Evaluation of Public Business Assistance in the Founding Process

This paper investigates economic and subjective effects of public business assistance delivered to nascent entrepreneurs in Germany. Employing cluster analysis, we explore the actual scope and intensity of business assistance used. Then we analyze predictors of take-up and perceived usefulness taking into account the different patterns of utilized assistance. Finally, we assess economic effects by studying subsequent business performance employing propensity score matching. We cannot reveal that business assistance translates into better start-up performance. However, we find that a lack of personal entrepreneurial resources predicts take-up of business assistance in general as well as perceived usefulness of comprehensive business assistance.entrepreneurship, business assistance, policy evaluation, entrepreneurial resources, big five

Innovative online platforms: Research opportunities

Economic growth in many countries is increasingly driven by successful startups that operate as online platforms. These success stories have motivated us to define and classify various online platforms according to their business models. This study discusses strategic and operational issues arising from five types of online platforms (resource sharing, matching, crowdsourcing, review, and crowdfunding) and presents some research opportunities for operations management scholars to explore

Piracy of Digital Products: A Critical Review of the Economics Literature

Digital products have the property that they can be copied almost costlessly. This makes them candidates for non-commercial copying by final consumers. Because the copy of a copy typically does not deteriorate in quality, copying products can become a wide-spread phenomenon – this can be illustrated by the surge of file-sharing networks. In this paper we provide a critical overview of the literature that addresses the economic consequences of end-user copying. We conclude that some models with network effects are well-suited for the analysis of software copying while other models incorporating the feature that copies provide information about the originals may be useful for the analysis of digital music copying.information good, piracy, copyright, internet, peer-to-peer, software, music