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Found 8 papers, 3 papers with code
Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks
Nevertheless, addressing CD in dynamic graphs remains an open problem, since the evolution of the network connections may poison the identification of communities, which may be evolving at a slower pace.
LogPrécis: Unleashing Language Models for Automated Malicious Log Analysis
In this paper, we systematically study how to benefit from the state-of-the-art in LM to automatically analyze text-like Unix shell attack logs.
GCNH: A Simple Method For Representation Learning On Heterophilous Graphs
Yet, achievement of consistent GNN performance on heterophilous graphs remains an open research problem.
Ranked #7 on
Node Classification
on Cornell
Node Classification on Non-Homophilic (Heterophilic) Graphs
Representation Learning
Recommendation Systems in Libraries: an Application with Heterogeneous Data Sources
The Reading&Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve the users' experience.
2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance
In this work, we highlight the limitations of the widely used homophily ratio and the recent Cross-Class Neighborhood Similarity (CCNS) metric in estimating GNN performance.
RL-IoT: Reinforcement Learning to Interact with IoT Devices
RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems.
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations.
Multidiscipinary Optimization For Gas Turbines Design
The authors present different strategies and algorithms that have been implemented exploiting the PD phase as a real-like design benchmark to illustrate results.
