EDBT 2026 Demo / reviewers in the wild / expert
Mia Weaver
dblp:268/5909
· DBLP profile ↗
4ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0000-6034-3927ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Computer networks · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Internet architecture and protocols · 77% Network measurement and analytics · 23% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet architecture and protocols
peering |
1.0 | 1 | 2026 | Take the Long Way Home - Distant Peering to the Cloud · IEEE Trans. Netw. 2026 |
Performance modeling and evaluation › simulation › communication system simulation
network simulation |
0.5 | 1 | 2021 | Software for Agent-based Network Simulation and Visualization · AAAI 2021 |
Network measurement and analytics › active measurement
traceroute measurement |
0.3 | 1 | 2026 | Take the Long Way Home - Distant Peering to the Cloud · IEEE Trans. Netw. 2026 |
Methods — techniques the papers use, named apart from their topics
cloud-based traceroute · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Take the Long Way Home - Distant Peering to the CloudabstractThe emergence of large cloud providers in the last decade has transformed the Internet, resulting in a seemingly ever-growing set of datacenters, points of presence, and network peers. Despite the availability of closer peering locations, some networks continue to peer with cloud providers at distant locations, traveling thousands of kilometers. In this paper, we employ a novel cloud-based traceroute campaign to characterize the distances networks travel to peer with the cloud. This unique approach allows us to gain unprecedented insights into the peering patterns of networks. Our findings reveal that 50% of the networks peer within 300 kilometers of the nearest datacenter. However, our analysis also reveals that over 20% of networks travel at least 6,700 kilometers beyond the proximity of the nearest computing facility, and some as much as 18,791 kilometers! While these networks connect with the cloud worldwide, from South America to Europe and Asia, many come to peer with cloud providers in North America, even from Oceania and Asia. We explore possible motivations for the persistence of distant peering, discussing factors such as cost-effective routes, enhanced peering opportunities, and access to exclusive content. Esteban Carisimo, Mia Weaver, Fabián E. Bustamante, Paul Barford |
IEEE Trans. Netw. | 2 |
| 2025 | Unsteady Underwater - On the Constancy of Submarine Path Properties
Mia Weaver, Paul Barford, Fabián E. Bustamante, Esteban Carisimo, Lynne Stokes, Weili Wu 0004 |
Networking | 1 |
| 2021 | Software for Agent-based Network Simulation and Visualization
Patrick Shepherd, Isaac Batts, Judy Goldsmith, Emory Hufbauer, Mia Weaver, Angela Zhang |
AAAI | 5 |
| 2020 | An Investigation into the Sensitivity of Social Opinion Networks to Heterogeneous Goals and PreferencesabstractAs research into the dynamics and properties of opinion diffusion on social networks has increased, so too has the attention paid to modeling such systems. Simulations using agent-based modeling (ABM) analyze aggregate network outcomes when individual agents act on typically limited information, and tend to focus on agents that are conforming and homophilic - that is, they prefer to be around similar others, and they update their own personal state over time to be more like their friends. In this work, we illustrate the value of diverse agent modeling in environments that allow for strategic unfriending. We focus on network dynamics generated by three agent models, or archetypes. Our work shows that polarization and consensus dynamics, as well as topological clustering effects, may rely more than previously known on the interplay between individuals' goals for the composition of their neighborhood's opinions. Patrick Shepherd, Mia Weaver, Judy Goldsmith |
ASONAM | 2 |