TY - GEN
T1 - Within-network classification using local structure similarity
AU - Desrosiers, Christian
AU - Karypis, George
PY - 2009
Y1 - 2009
N2 - Within-network classification, where the goal is to classify the nodes of a partly labeled network, is a semi-supervised learning problem that has applications in several important domains like image processing, the classification of documents, and the detection of malicious activities. While most methods for this problem infer the missing labels collectively based on the hypothesis that linked or nearby nodes are likely to have the same labels, there are many types of networks for which this assumption fails, e.g., molecular graphs, trading networks, etc. In this paper, we present a collective classification method, based on relaxation labeling, that classifies entities of a network using their local structure. This method uses a marginalized similarity kernel that compares the local structure of two nodes with random walks in the network. Through experimentation on different datasets, we show our method to be more accurate than several state-of-the-art approaches for this problem.
AB - Within-network classification, where the goal is to classify the nodes of a partly labeled network, is a semi-supervised learning problem that has applications in several important domains like image processing, the classification of documents, and the detection of malicious activities. While most methods for this problem infer the missing labels collectively based on the hypothesis that linked or nearby nodes are likely to have the same labels, there are many types of networks for which this assumption fails, e.g., molecular graphs, trading networks, etc. In this paper, we present a collective classification method, based on relaxation labeling, that classifies entities of a network using their local structure. This method uses a marginalized similarity kernel that compares the local structure of two nodes with random walks in the network. Through experimentation on different datasets, we show our method to be more accurate than several state-of-the-art approaches for this problem.
KW - Network
KW - Random walk
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=70350627219&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350627219&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04180-8_34
DO - 10.1007/978-3-642-04180-8_34
M3 - Conference contribution
AN - SCOPUS:70350627219
SN - 3642041795
SN - 9783642041792
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 260
EP - 275
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings
T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009
Y2 - 7 September 2009 through 11 September 2009
ER -