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. Network diffusion, such as spread of ideas, rumors, contagious disease, or a new type of behaviors, is one of the new type of behaviors, is one of the fundamental types within behaviors. Networks. Designing effective strategies for influence spread maximization, rumor spread minimization, or epidemic immunization has attracted substantial 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 are 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 netw 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 significant outperforms state-of-the-art models both in terms of recovering the latent network structure and information pathway tracking.