Heterogeneous network analysis on academic collaboration networks

University of Technology Sydney. Faculty of Engineering and Information Technology.Heterogeneous networks are a type of complex network model which can have multi-type objects and relationships. Nowadays, research on heterogeneous networks has been increasingly attracting interest because these networks are more advantageous in modeling real-world situations than traditional networks, that is homogenous networks, that can only have one type of object and relationship. For example, the network of Facebook has vertices including photographs, companies, movies, news and messages and different relationships among these objects. Besides that, heterogeneous networks are especially useful for representing complex abstract concepts, such as friendship and academic collaboration. Because these concepts are hard to measure directly, heterogeneous networks are able to represent these abstract concepts by concrete and measurable objects and relationships. Because of these features, heterogeneous networks are applied in many areas including social networks, the World Wide Web, research publication networks and so on. This motivates the thesis to work on network analysis in the context of heterogeneous networks. In the past, homogeneous networks were the research focus of network analysis and therefore many methods proposed by previous studies for social network analysis were designed for homogenous networks. Although heterogeneous networks can be considered as an extension of homogenous networks, most of these methods are not applicable on heterogeneous networks because these methods can only address one type of object and relationships instead of dealing with multi-type ones. In network analysis, there are three basic problems including community detection, link prediction and object ranking. These three questions are the basis of many practical questions, such as network structure extraction, recommendation systems and search engines. Community detection, also called clustering, aims to find the community structure of a network including subgroups of vertices that are closely related, which can facilitate people to understand the structure of networks. Link prediction is a task for finding links which are currently non-existent in networks but may appear in the future. Object ranking can be viewed as an object evaluation task which aims to order a set of objects based on their importance, relevance, or other user defined criteria. In addition to these three research issues, approaches for determining the number of clusters a priori is also important because it can improve the quality of community detection significantly. This thesis works on heterogeneous network and proposes a set of methods to address the four main research problems in network analysis including community detection, determining the number of clusters, link prediction and object ranking. There are four contributions in this thesis. Contribution 1 proposes a Multiple Semantic-path Clustering method which can facilitate users to achieve a desired clustering in heterogeneous networks. Contribution 2 develops a Leader Detection and Grouping Clustering method which can determine the number of clusters a priori, thereby improving the quality of clustering. Contribution 3 introduces a Network Evolution-based Link Prediction method which can improve link prediction accuracy by modeling evolution patterns of objects. Contribution 4 proposes a co-ranking method which can work on complex bipartite heterogeneous networks where one type of vertex can connect to themselves directly and indirectly. The performance of all developed methods in the thesis in terms of clustering quality, link prediction accuracy and ranking effectiveness, is evaluated in the context of a research management dataset of University of Technology, Sydney (UTS) and public bibliographic DBLP (DataBase systems and Logic Programming) dataset. Moreover, all the results of the proposed methods in this thesis are compared with state-of-the-art methods and these experimental results suggest that the proposed methods outperform these state-of-the-art methods in quantitative and qualitative analysis

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

Coauthor prediction for junior researchers

Research collaboration can bring in different perspectives and generate more productive results. However, finding an appropriate collaborator can be difficult due to the lacking of sufficient information. Link prediction is a related technique for collaborator discovery; but its focus has been mostly on the core authors who have relatively more publications. We argue that junior researchers actually need more help in finding collaborators. Thus, in this paper, we focus on coauthor prediction for junior researchers. Most of the previous works on coauthor prediction considered global network feature and local network feature separately, or tried to combine local network feature and content feature. But we found a significant improvement by simply combing local network feature and global network feature. We further developed a regularization based approach to incorporate multiple features simultaneously. Experimental results demonstrated that this approach outperformed the simple linear combination of multiple features. We further showed that content features, which were proved to be useful in link prediction, can be easily integrated into our regularization approach. © 2013 Springer-Verlag

A Model of Collaboration Network Formation with Heterogenous Skills

Collaboration networks provide a method for examining the highly heterogeneous structure of collaborative communities. However, we still have limited theoretical understanding of how individual heterogeneity relates to network heterogeneity. The model presented here provides a framework linking an individual's skill set to her position in the collaboration network, and the distribution of skills in the population to the structure of the collaboration network as a whole. This model suggests that there is a non-trivial relationship between skills and network position: individuals with a useful combination of skills will have a disproportionate number of links in the network. Indeed, in some cases, an individual's degree is non-monotonic in the number of skills she has--an individual with very few skills may outperform an individual with many. Special cases of the model suggest that the degree distribution of the network will be skewed, even when the distribution of skills is uniform in the population. The degree distribution becomes more skewed as problems become more difficult, leading to a community dominated by a few high-degree superstars. This has striking implications for labor market outcomes in industries where production is largely the result of collaborative effort