Impact of license choice on Open Source Software development activity
By Pietro Panzarasa, Tore Opsahl, Kathleen M. Carley. 2009
387 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/3f2aef379ffbceaeb6a7f1c6aaff83f27572d76a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Giovanni Romano
- Claudio Carpineto
- Tamás Vicsek
- Yanhong Wu
- Liang Gou
- Aravind Sankar
- Tamás Nepusz
- Stanislaw Osinski
In Information Systems
- Giovanni Romano
- Claudio Carpineto
- Stanislaw Osinski
- Dawid Weiss
- Yanhong Wu
- Akshay Deepak
- Dr. Hiteshwar Kumar Azad
- Rainer Brüggemann
Citations in the corpus
- Controlling edge dynamics in complex networks (2011, background): “UCIOnline 1,899 20,296 [49] A sent emails to B 33.”
- Exploring consumer preferences towards electric vehicles: The influence of consumer innovativeness (2016, neutral): “The concept of innate innovativeness has been empirically tested to determine its influence over the adoption of innovations, with the evidence suggesting that innate innovativeness is a significant factor (Manning et al., 1995; Lu et al., 2005, van Rijnsoever and Donders, 2009).”
- The Monetary Benefit of Early Flood Warnings in Europe (2015, neutral): “In addition, in flood situations many decisions do not necessarily achieve the best possible outcome measured in monetary terms, as decisions can be made under duress (Choo, 2009) or influenced by other external limitations (e.g. availability of temporary flood barriers).”
- A survey of Web clustering engines (2009, background): “As discussed in the preceding section, the focus of such experiments is on modeling an interactive information-.nding task and measuring relative performance [Carpineto et al. 2006, 2009; Otterbacher et al. 2006].”
- DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks (2020, methodology): “We find the attention weights to be biased towards recent snapshots in Yelp, while being more uniformly distributed in UCI.”