Building Trusted Startup Teams from LinkedIn Attributes: A Higher Order Probabilistic Analysis
Source of Publication
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
© 2020 IEEE. Startups arguably contribute to the current business landscape by developing innovative products and services. The discovery of business partners and employees with a specific background which can be verified stands out repeatedly as a prime obstacle. LinkedIn is a popular platform where professional milestones, endorsements, recommendations, and skills are posted. A graph search algorithm with a BFS and a DFS strategy for seeking trusted candidates in LinkedIn is proposed. Both strategies rely on a metric for assessing the trustworthiness of an account according to LinkedIn attributes. Also, a stochastic vertex selection mechanism reminiscent of preferential attachment guides search. Both strategies were verified against a large segment of the vivid startup ecosystem of Patras, Hellas. A higher order probabilistic analysis suggests that BFS is more suitable. Findings also imply that emphasis should be given to local networking events, peer interaction, and to tasks allowing verifiable credit for the respective work.
Business | Computer Sciences | Electrical and Computer Engineering | Social and Behavioral Sciences
graph mining, higher order statistics, linked data, LinkedIn API, multilayer graphs, probabilistic analysis, trust
Drakopoulos, Georgios; Kafeza, Eleana; Mylonas, Phivos; and Al Katheeri, Haseena, "Building Trusted Startup Teams from LinkedIn Attributes: A Higher Order Probabilistic Analysis" (2020). All Works. 786.
Indexed in Scopus