Jingchao Ni

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Integrating multiple graphs (or networks) has been shown to be a promising approach to improve the graph clustering accuracy. Various multi-view and multi-domain graph clustering methods have recently been developed to integrate multiple networks. In these methods, a network is treated as a view or domain.The key assumption is that there is a common(More)
Networks are prevalent and have posed many fascinating research questions. How can we spot similar users, e.g., virtual identical twins, in Cleveland for a New Yorker? Given a query disease, how can we prioritize its candidate genes by incorporating the tissue-specific protein interaction networks of those similar diseases? In most, if not all, of the(More)
Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multi-domain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different(More)
Automating medical diagnosis is an important data mining problem, which is to infer likely disease(s) for some observed symptoms. Algorithms to the problem are very beneficial as a supplement to a real diagnosis. Existing diagnosis methods typically perform the inference on a sparse bipartite graph with two sets of nodes representing diseases and symptoms,(More)
Detecting system anomalies is an important problem in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be powerful in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two(More)
Accurately prioritizing candidate disease genes is an important and challenging problem. Various network-based methods have been developed to predict potential disease genes by utilizing the disease similarity network and molecular networks such as protein interaction or gene co-expression networks. Although successful, a common limitation of the existing(More)
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