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Information diffusion network inferring and pathway tracking

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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Provenance Identification in diffusion networks with Incomplete Cascades

Provenance Identification in Diffusion Networks with Incompl...

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第37届中国控制会议

作者： Chaoyi Shi Qi Zhang Tianguang Chu College of Engineering Peking University School of Information Technology & Management University of International Business & Economics

We consider the problem of provenance identification for diffusion processes on social networks based on cascade information observed from a small set of nodes during the observation windows. The diffusion dynamics are described by the susceptible-infected(SI) model and a constrained maximum likelihood(ML) estimator is formulated to maximize the probability of the diffusion provenance. The identification approach consists of two steps: first, a pruning rule is defined to obtain a set of suspected provenance nodes from susceptible node observers;and then a correlation coefficient is maximized to find the provenance node from the candidate ones. Experiments on synthetic networks and real-world networks verify the effectiveness of our approach.

关键词： Provenance Identification diffusion network Susceptible-infected(SI) Model Maximum Likelihood(ML) Estimator Pruning Rule

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