loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock
A Multi-agent Approach for Graph Classification

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Learning Paradigms and Algorithms; Stochastic Learning and Statistical Algorithms

Authors: Luca Baldini and Antonello Rizzi

Affiliation: Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza", Via Eudossiana 18, 00184 Rome, Italy

Keyword(s): Multi-agent Systems, Graph Embedding, Supervised Learning, Structural Pattern Recognition.

Abstract: In this paper, we propose and discuss a prototypical framework for graph classification. The proposed algorithm (Graph E-ABC) exploits a multi-agent design, where swarm of agents (orchestrated via evolutionary optimization) are in charge of finding meaningful substructures from the training data. The resulting set of substructures compose the pivotal entities for a graph embedding procedure that allows to move the pattern recognition problem from the graph domain towards the Euclidean space. In order to improve the learning capabilities, the pivotal substructures undergo an independent optimization procedure. The performances of Graph E-ABC are addressed via a sensitivity analysis over its critical parameters and compared against current approaches for graph classification. Results on five open access datasets of fully labelled graphs show interesting performances in terms of accuracy, counterbalanced by a relatively high number of pivotal substructures.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.117.154.229

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Baldini, L. and Rizzi, A. (2021). A Multi-agent Approach for Graph Classification. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - NCTA; ISBN 978-989-758-534-0; ISSN 2184-3236, SciTePress, pages 334-343. DOI: 10.5220/0010677300003063

@conference{ncta21,
author={Luca Baldini. and Antonello Rizzi.},
title={A Multi-agent Approach for Graph Classification},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - NCTA},
year={2021},
pages={334-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010677300003063},
isbn={978-989-758-534-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - NCTA
TI - A Multi-agent Approach for Graph Classification
SN - 978-989-758-534-0
IS - 2184-3236
AU - Baldini, L.
AU - Rizzi, A.
PY - 2021
SP - 334
EP - 343
DO - 10.5220/0010677300003063
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic