This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.
Iterative classification algorithm (ICA) is an iterative method that is widely applied and extended in many studies @cite_15 @cite_12 @cite_10 . The basic assumption of ICA is that, given the labels of a node's neighbors, the label of the node is independent of the features of its neighbors and non-neighbors, and the labels of all non-neighbors. In ICA, each node is expressed by combining the node features and relational features constructed by using the labels of all the neighbors of the node. The relational features can be computed by using an aggregation function over the neighbors, such as @math , @math , @math and so on. Based on the node features and relational features, ICA trains a classifier and iteratively updates the predictions of all nodes by using the predictions for node with unknown labels. This process continues until the algorithm converges. In this work, we adopt an ICA-like algorithm to perform collective classification with focuses on transferring structure knowledge from the source network to improve collective classification accuracy on the target network, under the assumption that the number of labeled nodes is very limited.
@cite_15 @cite_10 @cite_12
2121250409 2143387805 2133075925
Relational data offer a unique opportunity for improving the c lassification accuracy o f statistical m odels. If two objects are related, inferring something about one object can aid inferences about the other. We present an iterative classification p rocedure that exploits this characteristic of relational data. This approach uses simple Bayesian classifiers in an iterative fashion, dynamically upd ating the attributes of some objects as inferences are made about related ob jects. Inferences made with h igh confidence in initial iterations are fed back into the data and are used to inform subsequent i nferences about related ob jects. We evaluate the performance of this approach on a binary classification task. Experiments indicate that it erative classification significantly increases accuracy when compared to a single-pass approach. We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the labels of linked nodes are correlated, and the goal is to exploit these dependencies and accurately label the nodes. This problem arises in many domains, including social and biological network analysis and document classification, and there has been much recent interest in methods that collectively classify the nodes in the network. While in many cases labeled examples are expensive, often network information is available. We show how an active learning algorithm can take advantage of network structure. Our algorithm effectively exploits the links between instances and the interaction between the local and collective aspects of a classifier to improve the accuracy of learning from fewer labeled examples. We experiment with two real-world benchmark collective classification domains, and show that we are able to achieve extremely accurate results even when only a small fraction of the data is labeled. The problems of object classification (labeling the nodes of a graph) and link prediction (predicting the links in a graph) have been largely studied independently. Commonly, object classification is performed assuming a complete set of known links and link prediction is done assuming a fully observed set of node attributes. In most real world domains, however, attributes and links are often missing or incorrect. Object classification is not provided with all the links relevant to correct classification and link prediction is not provided all the labels needed for accurate link prediction. In this paper, we propose an approach that addresses these two problems by interleaving object classification and link prediction in a collective algorithm. We investigate empirically the conditions under which an integrated approach to object classification and link prediction improves performance, and find that performance improves over a wide range of network types, and algorithm settings.