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topological features Based Entity Disambiguation

Journal of Computer Science & Technology 2016年第5期31卷 1053-1068页

作者： Chen-Chen Sun De-Rong Shen Yue Kou Tie-Zheng Nie Ge Yu School of Computer Science and Engineering Northeastern University Shenyang 110819 China

This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy.

关键词： entity disambiguation topological feature clustering random walk with restarts

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Information diffusion network inferring and pathway tracking

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Science China(Information Sciences) 2015年第9期58卷 135-149页

作者： ZHOU DongHao HAN WenBao WANG YongJun YUAN BaoDi College of Computer National University of Defense Technology State Key Laboratory of Mathematical Engineering and Advanced Computing College of Computer Science and Technology Fudan University

Network diffusion, such as spread of ideas, rumors, contagious disease, or a new type of behaviors, is one of the fundamental processes within networks. Designing effective strategies for influence spread maximization, rumor spread minimization, or epidemic immunization has attracted considerable research attention. However, a key challenge is that many times we can only observe the trace of contagion spreading across network, but the underlying network structure is unknown and the transmission rates between node pairs are unclear to us. In this paper, given the observed information cascades, we aim to address two problems:diffusion network structure inferring and information diffusion pathways tracking. We propose a novel probabilistic model called Network Inferring from Multidimensional features of Cascades(NIMFC) which takes into account heterogeneous features, including temporal and topological features of cascades, node attributes, and information content, to infer the latent network structure and transmission rates of edges. Also, based on the inferred network structure, we may track diffusion pathways of a cascade in social networks. We use blocked coordinate descent method to learn a sparse estimation of the latent network. Our proposed model NIMFC is evaluated both on large synthetic and real-world data sets, and experimental results show that our method significantly outperforms state-of-the-art models both in terms of recovering the latent network structure and information pathway tracking.

关键词： diffusion network network inferring information pathway tracking temporal feature node attributes topological feature informatio

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