Co-author Relationship Prediction in Heterogeneous Bibliographic Networks

By Yizhou Sun, R. Barber, Manish Gupta, Charu C. Aggarwal, Jiawei Han. International Conference on Advances in Social Networks Analysis and Mining, 2011

462 citations recorded by the source corpus.

The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, \i.e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (\e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called\emph{Path Predict}, \i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/88ad9c98f58369373699c16c8bcf37c640620d58

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Citations in the corpus

Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, methodology): “0 ( Himmelstein and Baranzini, 2015a ; Sun et al., 2011 ).”
Recommender Systems (2016, unknown): “A large and growing area of research is in the context of heterogeneous networks [36, 576, 577] in which links of multiple types are predicted from one another.”
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (2017, background): “The concept of meta-paths has been proposed to describe how nodes are related to each other [18] [26].”
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification (2016, background): “Examples include bibliographic networks [20, 22], movie recommendation networks [32] and many online social networks containing information of heterogeneous types [19].”
Attention Models in Graphs (2018, methodology): “bedding matrix is denoted as Z. The learned node embeddings given as output can subsequently be used as input to mining and machine learning algorithms for a variety of tasks such as link prediction [Sun et al. 2011], classification [Velickovic et al. 2018], community detection [Girvan and Newman200”