Authors:
Michalis Nikolaou
1
;
Georgios Drakopoulos
2
;
Phivos Mylonas
3
and
Spyros Sioutas
1
Affiliations:
1
Computer Engineering and Informatics Department, University of Patras, Patras, Greece
;
2
Department of Informatics, Ionian Univerity, Kerkyra, Greece
;
3
Informatics and Computer Engineering Department, University of West Attica, Athens, Greece
Keyword(s):
Intelligent Agents, Network Structural Integrity, Connectivity Patterns, Link Prediction, Graph Mining, Neo4j.
Abstract:
Intelligent agents (IAs) are highly autonomous software applications designed for performing tasks in a broad spectrum of virtual environments by circulating freely around them, possibly in numerous copies, and taking actions as needed, therefore increasing human digital awareness. Consequently, IAs are indispensable for large scale digital infrastructure across fields so diverse as logistics and long supply chains, smart cities, enterprise and Industry 4.0 settings, and Web services. In order to achieve their objectives, frequently IAs rely on machine learning algorithms. One such prime example, which lies in the general direction of evaluating the network structure integrity, is link prediction, which depending on the context may denote growth potential or a malfunction. IAs employing machine learning algorithms and local structural graph attributes pertaining to connectivity patterns are presented. Their performance is evaluated with metrics including the F1 score and the ROC curv
e on a benchmark dataset of scientific citations provided by Neo4j containing ground truth.
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