An Assessment of Node Classification Accuracy in Social Networks Using Label-Dependent Feature Extraction

Kajdanowicz, Tomasz; Kazienko, Przemysław; Doskocz, Piotr; Litwin, Krzysztof

doi:10.1007/978-3-642-16318-0_14

Cited by 3 publications

(1 citation statement)

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“…Previous authors have looked at the problem of classifying nodes in social networks (e.g., [38], [39]). In these approaches, both network structure information and node class labels are combined to provide new features to improve classification [40].…”

Section: Related Workmentioning

confidence: 99%

Leveraging Network Properties for Trust Evaluation in Multi-agent Systems

Wang

Maghami

Sukthankar

2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-In this paper, we present a collective classification approach for identifying untrustworthy individuals in multiagent communities from a combination of observable features and network connections. Under the assumption that data are organized as independent and identically distributed (i.i.d.) samples, traditional classification is typically performed on each object independently, without considering the underlying network connecting the instances. In collective classification, a set of relational features, based on the connections between instances, is used to augment the feature vector used in classification. This approach can perform particularly well when the underlying data exhibits homophily, a propensity for similar items to be connected. We suggest that in many cases human communities exhibit homophily in trust levels since shared attitudes toward trust can facilitate the formation and maintenance of bonds, in the same way that other types of shared beliefs and value systems do. Hence, knowledge of an agent's connections provides a valuable cue that can assist in the identification of untrustworthy individuals who are misrepresenting themselves by modifying their observable information. This paper presents results that demonstrate that our proposed trust evaluation method is robust in cases where a large percentage of the individuals present misleading information.

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Section: Related Workmentioning

confidence: 99%