Giulio Grassi
- Computer Networks and Communications top 10%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Materials Chemistry
- Co-authors
- Giovanni PauLuca MuscarielloGiovanna CarofiglioJordan AugéLucas WangRama VuyyuruRyuji WakikawaLixia Zhang
- Topics
- Opportunistic and Delay-Tolerant Networks (5 papers)Caching and Content Delivery (5 papers)Peer-to-Peer Network Technologies (2 papers)
- Journals
- ACM SIGCOMM Computer Communication ReviewIEEE Transactions on Network and Service ManagementHAL (Le Centre pour la Communication Scientifique Directe)
- Partner nations
- United StatesFranceItaly
In The Last Decade
Giulio Grassi
7 papers receiving 122 citations
Peers
Comparison fields: 5 of 22
- Computer Networks and Communications 117
- Electrical and Electronic Engineering 29
- Computer Vision and Pattern Recognition 9
- Artificial Intelligence 5
- Materials Chemistry 5
Countries citing papers authored by Giulio Grassi
This map shows the geographic impact of Giulio Grassi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Giulio Grassi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giulio Grassi more than expected).
Fields of papers citing papers by Giulio Grassi
This network shows the impact of papers produced by Giulio Grassi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Giulio Grassi. The network helps show where Giulio Grassi may publish in the future.
Co-authorship network of co-authors of Giulio Grassi
This figure shows the co-authorship network connecting the top 25 collaborators of Giulio Grassi. A scholar is included among the top collaborators of Giulio Grassi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Giulio Grassi. Giulio Grassi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 13 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 52 | |
| 6 | 2 | |
| 7 | 48 | |
| 8 | 8 |
About Giulio Grassi
Giulio Grassi is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 8 papers that have together received 126 indexed citations. Recurring topics across this work include Opportunistic and Delay-Tolerant Networks (5 papers), Caching and Content Delivery (5 papers) and Peer-to-Peer Network Technologies (2 papers). The work is most often cited by research in Computer Networks and Communications (117 citations), Software (3 citations) and Electrical and Electronic Engineering (29 citations). Giulio Grassi has collaborated with scholars based in United States, France and Italy. Frequent co-authors include Giovanni Pau, Luca Muscariello, Giovanna Carofiglio, Jordan Augé, Lucas Wang, Rama Vuyyuru, Ryuji Wakikawa, Lixia Zhang, Davide Pesavento and Michele Papalini. Their work appears in journals such as ACM SIGCOMM Computer Communication Review, IEEE Transactions on Network and Service Management and HAL (Le Centre pour la Communication Scientifique Directe).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.