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Many link prediction methods have been developed to infer unobserved links or predict missing links based on the observed network structure that is always incomplete and subject to interfering noise. Thus, the performance of existing methods is usually limited in that their computation depends only on input graph structures, and they do not consider external information. The effects of social influence and homophily suggest that both network structure and node attribute information should help to resolve the task of link prediction. This work proposes SASNMF, a link prediction unified framework based on non-negative matrix factorization that considers not only graph structure but also the internal and external auxiliary information, which refers to both the node attributes and the structural latent feature information extracted from the network. Furthermore, three different combinations of internal and external information are proposed and input into the framework to solve the link prediction problem. Extensive experimental results on thirteen real networks, five node attribute networks and eight non-attribute networks show that the proposed framework has competitive performance compared with benchmark methods and state-of-the-art methods, indicating the superiority of the presented algorithm., Competing Interests: The authors have declared that no competing interests exist.

Finding semantic communities using network topology and contents together is a hot topic in community detection. Existing methods often use word attributes in an indiscriminate way to help finding communities. Through analysis we find that, words in networked contents often embody a hierarchical semantic structure. Some words reflect a background topic of the whole network with all communities, some imply the high-level general topic covering several topic-related communities, and some imply the high-resolution specialized topic to describe each community. Ignoring such semantic structures often leads to defects in depicting networked contents where deep semantics are not fully utilized. To solve this problem, we propose a new Bayesian probabilistic model. By distinguishing words from either a background topic or some two-level topics (i.e., general and specialized topics), this model not only better utilizes the networked contents to help finding communities, but also provides a clearer multiplex semantic community interpretation. We then give an efficient variational algorithm for model inference. The superiority of this new approach is demonstrated by comparing with ten state-of-the-art methods on nine real networks and an artificial benchmark. A case study is further provided to show its strong ability in deep semantic interpretation of communities. [ABSTRACT FROM AUTHOR]

Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection. [ABSTRACT FROM AUTHOR]

On one hand, the detection of tightly connected groups, also known as community detection in complex networks, is a prominent problem for network analysis and mining. On the other hand, almost all of social, biological, bibliographic, communication and computer systems are modeled as temporal networks, the topological structures of which evolve with time, or multiplex networks, each pair of nodes of which has multiple linked relations. Current methods of community detection for temporal networks are based on incremental, independent or evolutionary clustering, and for multiplex networks are based on fusion of the multiple links. However, all these methods ignore the common structure hidden in the networks, which is denoted as the common cluster here. So in this paper, we propose a constrained common cluster based model ( C 3 model) to analyze the temporal and multiplex networks, which can not only detect the community structure, but also identify the importance of each node based on the common cluster structure of both two classes of networks. The intrinsic assumption of the proposed model is that there are common or coincident clusters hidden in these networks. In detail, we first construct the Markov steady-state matrices of each snapshot of temporal network or each slice of multiplex network. Next, we propose the object function of C 3 model by combining the Markov steady-state matrices, similarity matrices with community membership matrices of each snapshot or slice of the network. Last, a gradient descent algorithm based on non-negative matrix factorization is proposed for the object function. Experiments on both synthetic datasets and real-world networks demonstrate that the proposed C 3 model has competitive performance based on the evaluation indexes NMI and error of community detection, otherwise, the proposed model could identify the importance of nodes of the temporal or multiplex networks. [ABSTRACT FROM AUTHOR]

To understand time-evolving networks, researchers should not only concentrate on the community structures, an essential property of complex networks, in each snapshot, but also study the internal evolution of the entire networks. Temporal communities provide insights into such mechanism, i.e., how the communities emerge, expand, shrink, merge, split and decay over time. Based on the symmetric nonnegative matrix factorization (SNMF), we present a dynamic model to detect temporal communities, which not only could find a well community structure in a given snapshot but also demands the results bear some similarity to the partition obtained from the previous snapshot. Moreover, our method can handle the situation that of the number of community changes in the networks. Also, a gradient descent algorithm is proposed to optimize the objective function of the model. Experimental results on both the synthetic and real-world networks indicate that our method outperforms the state-of-art methods for temporal community detection. [ABSTRACT FROM AUTHOR]