Multi-label relational neighbor classification using social context features

By Xi Wang, Gita Reese Sukthankar. 2013

72 citations recorded by the source corpus.

Networked data, extracted from social media, web pages, and bibliographic databases, can contain entities of multiple classes, interconnected through different types of links. In this paper, we focus on the problem of performing multi-label classification on networked data, where the instances in the network can be assigned multiple labels. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, instances in a network can be linked for various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. In this paper, we propose a multi-label iterative relational neighbor classifier that employs social context features (SCRN). Our classifier incorporates a class propagation probability distribution obtained from instances' social features, which are in turn extracted from the network topology. This class-propagation probability captures the node's intrinsic likelihood of belonging to each class, and serves as a prior weight for each class when aggregating the neighbors' class labels in the collective inference procedure. Experiments on several real-world datasets demonstrate that our proposed classifier boosts classification performance over common benchmarks on networked multi-label data.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Zhu
2. M. Murty
3. Juzheng Li
4. Sharad Nandanwar
5. Eliav Buchnik
6. Edith Cohen
7. Hang Su
8. Tianyu Pang

In Information Systems

1. Jun Zhu
2. M. Murty
3. Juzheng Li
4. Sharad Nandanwar

Citations in the corpus

• DeepWalk: online learning of social representations (2014, neutral): “The most relevant relational classification algorithms to our work incorporate community information by learning clusters [33], by adding edges between nearby nodes [14], by using PageRank [24], or by extending relational classification to take additional features into account [43].”
• Structural Neighborhood Based Classification of Nodes in a Network (2016, methodology): “SCRN [25]: SCRN, an approach for collective classification, exploits social context features in relational learning.”
• Local Modeling of Attributed Graphs: Algorithms and Applications (2016, unknown): “The most relevant relational classification algorithms to our work incorporate community information by learning clusters [103], by adding edges between nearby nodes [49], by using PageRank [81], or by extending relational classification to take additional features into account [144].”
• Discriminative Deep Random Walk for Network Classification (2016, background): “(Wang and Sukthankar, 2013) extend the conventional relational classification to consider more additional features.”