Most start-ups fail, and early-stage ventures face even lower survival rates. Identifying high-potential start-ups remains a critical challenge for venture capital (VC) investors and policymakers. While predictive models exist, the evolving relationships between VC investors, start-ups, and management teams in dynamic networks are underexplored. We propose a method to predict whether a start-up will succeed within 5 years of its first funding round. Using a 40-year global VC dataset, we model the VC ecosystem as a dynamic bipartite network linking start-ups to individuals (investors/managers). Our approach incrementally updates graph embeddings through unsupervised self-attention to incorporate new nodes, edges, and their neighbors. Node embeddings are further fine-tuned via link prediction and classification tasks, while temporal dependencies are captured to form sequential representations. The model identifies early-stage start-ups with twice the success likelihood of those chosen by professional investors. Key factors including networking and education align with VC literature. Additionally, we provide model complexity analysis and open source our implementation to support practical applications and future research.

Seeking online health information may reinforce the anxiety of those who are already overly anxious about their health. This study explored how people with health anxiety may behave differently in terms of their attentional biases when seeking health information online. We conducted an eye-tracking experiment with 17 participants in the high health anxious group and 17 participants in the low health anxious group, who performed three types of information-seeking tasks (factual, interpretive, and exploratory) on a Chinese health website. We observed that both groups mainly allocated their attention to the stages of evaluating the list of search results and synthesizing information to make health decisions. They showed similar attention tracks at the earlier search stages and health anxiety was found to associate with attentional biases towards certain website stimuli. However, the high health anxious group showed more active eye movements than their low health anxious counterparts. Attentional biases from the high health anxious group mainly occurred at the later stage of processing rather than the initial orientation stages. As for task types, the high health anxious group presented more extensive attentional biases when performing the interpretive task, compared to the explorative and factual tasks. The findings provide novel insights into the attentional biases of people with health anxiety as they search online for health information, which have implications on designing more effective information interventions for vulnerable groups of health information consumers. The findings can also help clinicians interpret patients’ anxiety-related sensations and provide intervening recommendations for clients in use of online health information.

OBJECTIVE: Translational science aims at "translating" basic scientific discoveries into clinical applications. The identification of translational science has practicality such as evaluating the effectiveness of investments made into large programs like the Clinical and Translational Science Awards. Despite several proposed methods that group publications-the primary unit of research output-into some categories, we still lack a quantitative way to place articles onto the full, continuous spectrum from basic research to clinical medicine. MATERIALS AND METHODS: I learn vector representations of controlled vocabularies assigned to Medline articles to obtain a translational axis that points from basic science to clinical medicine. The projected position of a term on the translational axis, expressed by a continuous quantity, indicates the term's "appliedness." The position of an article, determined by the average location over its terms, quantifies the degree of its appliedness, which I term the level score. RESULTS: I validate the present method by comparing with previous techniques, showing excellent agreement yet uncovering significant variations of scores of articles in previously defined categories. The measure allows us to characterize the standing of journals, disciplines, and the entire biomedical literature along the basic-applied spectrum. Analysis on large-scale citation network reveals 2 main findings. First, direct citations mainly occurred between articles with similar scores. Second, shortest paths are more likely ended up with an article closer to the basic end of the spectrum, regardless of where the starting article is on the spectrum. CONCLUSIONS: The proposed method provides a quantitative way to identify translational science.

Many "sharing economy" platforms, such as Uber and Airbnb, have become increasingly popular, providing consumers with more choices and suppliers a chance to make profit. They, however, have also brought about emerging issues regarding regulation, tax obligation, and impact on urban environment, and have generated heated debates from various interest groups. Empirical studies regarding these issues are limited, partly due to the unavailability of relevant data. Here we aim to understand service providers of the sharing economy, investigating who joins and who benefits, using the Airbnb market in the United States as a case study. We link more than 211 thousand Airbnb listings owned by 188 thousand hosts with demographic, socio-economic status (SES), housing, and tourism characteristics. We show that income and education are consistently the two most influential factors that are linked to the joining of Airbnb, regardless of the form of participation or year. Areas with lower median household income, or higher fraction of residents who have Bachelor's and higher degrees, tend to have more hosts. However, when considering the performance of listings, as measured by number of newly received reviews, we find that income has a positive effect for entire-home listings; listings located in areas with higher median household income tend to have more new reviews. Our findings demonstrate empirically that the disadvantage of SES-disadvantaged areas and the advantage of SES-advantaged areas may be present in the sharing economy.

Link prediction in complex networks is an important issue in graph mining. It aims at estimating the likelihood of the existence of links between nodes by the know network structure information. Currently, most link prediction algorithms based on local information consider only the individual characteristics of common neighbors. In this paper, first, we study the link prediction results as the change of the exponent on the degree of common neighbors, and find some regular pattern between different networks and different exponent. After that, we come up with a new algorithm exploiting the interactions between common neighbors, namely Individual Attraction Index. To reduce the time complexity, we design a simple edition, called Simple Individual Attraction Index. We compare nine well-known local information metrics on eight real networks. The result proves well the best overall performance of these two new algorithms.

Decentralized search in networks is an important algorithmic problem in the study of complex networks and social networks analysis. It has a large number of practical applications, from shortest paths search in social network relationship, web pages search in WWW to querying files in peer-to-peer file sharing networks and so on. In this paper, we explore this problem from a perspective of community structure. We first find that through maximizing sample conductance, we can get high coverage sample. Based on this result, then, we propose a new decentralized search strategy named Conductance Search which tries to efficiently find the nodes belonging to different communities. We compare the strategy with other common strategies. And the results show that the conductance search outperforms others in number of steps to find the target and time complexity. Finally, we find some previous conclusions fail in many real-world networks and discuss network search-ability from the perspective of various structural properties.