activity
20152022
most citedO Peer, Where Art Thou? Uncovering Remote Peering Interconnections at IXPs

26 citations · 45 across the 9 of their papers we have counts for

collaborators

17 papers

cs.NI2022

Benchmarking Graph Neural Networks for Internet Routing Data

Dimitrios Panteleimon Giakatos, Sofia Kostoglou, Pavlos Sermpezis +1

The Internet is composed of networks, called Autonomous Systems (or, ASes), interconnected to each other, thus forming a large graph. While both the AS-graph is known and there is…

cs.NI2021

Network Friendly Recommendations: Optimizing for Long Viewing Sessions

Theodoros Giannakas, Pavlos Sermpezis, Thrasyvoulos Spyropoulos

Caching algorithms try to predict content popularity, and place the content closer to the users. Additionally, nowadays requests are increasingly driven by recommendation systems (…

cs.LG2021

Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning

Marinos Poiitis, Pavlos Sermpezis, Athena Vakali

Graph Representation Learning (GRL) has become essential for modern graph data mining and learning tasks. GRL aims to capture the graph's structural information and exploit it in c…

cs.NI2021

Estimating the Impact of BGP Prefix Hijacking

Pavlos Sermpezis, Vasileios Kotronis, Konstantinos Arakadakis +1

BGP prefix hijacking is a critical threat to the resilience and security of communications in the Internet. While several mechanisms have been proposed to prevent, detect or mitiga…

cs.NI2021

Fairness in Network-Friendly Recommendations

Theodoros Giannakas, Pavlos Sermpezis, Anastasios Giovanidis +2

As mobile traffic is dominated by content services (e.g., video), which typically use recommendation systems, the paradigm of network-friendly recommendations (NFR) has been propos…

cs.NI2020

Network-aware Recommendations in the Wild: Methodology, Realistic Evaluations, Experiments

Savvas Kastanakis, Pavlos Sermpezis, Vasileios Kotronis +2

Joint caching and recommendation has been recently proposed as a new paradigm for increasing the efficiency of mobile edge caching. Early findings demonstrate significant gains for…