Within-network classification using local structure similarity
Christian Desrosiers,George Karypis +1 more
- pp 260-275
TLDR
This paper presents a collective classification method, based on relaxation labeling, that classifies entities of a network using their local structure using a marginalized similarity kernel that compares the local structure of two nodes with random walks in the network.Abstract:
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.read more
Citations
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Label-dependent node classification in the network
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TL;DR: The algorithm HENPC is proposed, which is able to work on heterogeneous networks with an arbitrary structure, and extracts possibly overlapping and hierarchically-organized heterogeneous clusters and exploits them for predictive purposes.
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Node Classification in Social Network via a Factor Graph Model
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Mining frequent neighborhood patterns in a large labeled graph
Jialong Han,Ji-Rong Wen +1 more
TL;DR: This paper proposes a new class of patterns called frequent neighborhood patterns, which are free from the "DCP-intuitiveness" dilemma of mining frequent subgraphs in a single graph, and shows that the new patterns not only maintain DCP, but also have equally significant interpretations as subgraph patterns.
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