Authors:
Omar Ali
1
;
Giovanni Zappella
2
;
Tijl De Bie
1
and
Nello Cristianini
1
Affiliations:
1
Bristol University, United Kingdom
;
2
Università Degli Studi di Milano, Italy
Keyword(s):
Nodes classification, Social networks, Data mining, Machine learning.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Data Engineering
;
Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Missing Data
;
Natural Language Processing
;
On-Line Learning
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Software Engineering
;
Symbolic Systems
;
Theory and Methods
Abstract:
The task of predicting the label of a network node, based on the labels of the remaining nodes, is an area of growing interest in machine learning, as various types of data are naturally represented as nodes in a graph. As an increasing number of methods and approaches are proposed to solve this task, the problem of comparing their performance becomes of key importance. In this paper we present an extensive experimental comparison of 15 different methods, on 15 different labelled-networks, as well as releasing all datasets and source code. In addition, we release a further set of networks that were not used in this study (as not all benchmarked methods could manage very large datasets). Besides the release of data, protocols and algorithms, the key contribution of this study is that in each of the 225 combinations we tested, the best performance—both in accuracy and running time—was achieved by the same algorithm: Online Majority Vote. This is also one of the simplest methods to impl
ement.
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