Daniel Stutzbach

24 papers receiving 1.3k citations

Hit Papers

Understanding churn in peer-to-peer networks20062026201220192006100200300400

Peers

Daniel Stutzbach
Comparison fields: 5 of 45
  • Computer Networks and Communications 1.3k
  • Statistical and Nonlinear Physics 356
  • Artificial Intelligence 301
  • Information Systems 204
  • Sociology and Political Science 92
Replace Kun‐Lung Wu with:
Kun‐Lung Wu United States
Allan Heydon United States
Daniel R. Figueiredo Brazil
Flavio Chierichetti United States
Marco Rosa Italy
Ansley Post Germany
Enhua Tan United States
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Daniel Stutzbach relative to Kun‐Lung Wu United States Kun‐Lung Wu's profile →
Citations per field
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Citations per year

Countries citing papers authored by Daniel Stutzbach

Since Specialization
Citations

This map shows the geographic impact of Daniel Stutzbach'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 Daniel Stutzbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Stutzbach more than expected).

Fields of papers citing papers by Daniel Stutzbach

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Stutzbach. 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 Daniel Stutzbach. The network helps show where Daniel Stutzbach may publish in the future.

Co-authorship network of co-authors of Daniel Stutzbach

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Stutzbach. A scholar is included among the top collaborators of Daniel Stutzbach 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 Daniel Stutzbach. Daniel Stutzbach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 4
2 12
3 58
4 127
5 104
6
Evaluating Sampling Techniques for Large Dynamic Graphs
20
7 31
8 85
9
Understanding churn in peer-to-peer networksbreakdown →
476
10 31
11 61
12
Watching data streams toward a multi-homed sink under routing changes introduced by a BGP beacon
5
13 61
14
Peer-to-Peer Receiver-driven Mesh-based Streaming
88
15 41
16 8
17 48
18
Towards a Better Understanding of Churn in Peer-to-Peer Networks
18
19
Characterizing Today's Gnutella Topology
8
20
Swarming Scalable Content Delivery for the Masses
5

About Daniel Stutzbach

Daniel Stutzbach is a scholar working on Computer Networks and Communications, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 24 papers that have together received 1.4k indexed citations. Recurring topics across this work include Peer-to-Peer Network Technologies (22 papers), Caching and Content Delivery (21 papers) and Complex Network Analysis Techniques (7 papers). The work is most often cited by research in Computer Networks and Communications (1.3k citations), Statistical and Nonlinear Physics (356 citations) and Artificial Intelligence (301 citations). Daniel Stutzbach has collaborated with scholars based in United States and Germany. Frequent co-authors include Reza Rejaie, Subhabrata Sen, Nick Duffield, Walter Willinger, Nazanin Magharei, Yang Guo, Daniel Zappala, Z. Morley Mao, Timothy G. Griffin and Matthew Roughan. Their work appears in journals such as IEEE/ACM Transactions on Networking, Computer Networks and ACM SIGMETRICS Performance Evaluation Review.

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.

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