Link Prediction Problem In Networks Research Articles

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T+1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability

Networks derived from many disciplines, such as social relations, web contents, and cancer progression, are temporal and incomplete. Link prediction in temporal networks is of theoretical interest and practical significance because spurious links are critical for investigating evolving mechanisms. In this study, we address the temporal link prediction problem in networks, i.e. predicting links at time T+1 based on a given temporal network from time 1 to T. To address the relationships among matrix decomposition-based algorithms, we prove the equivalence between the eigendecomposition and nonnegative matrix factorization (NMF) algorithms, which serves as the theoretical foundation for designing NMF-based algorithms for temporal link prediction. A novel NMF-based algorithm is proposed based on such equivalence. The algorithm factorizes each network to obtain features using graph communicability, and then collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

A link prediction algorithm based on ant colony optimization

The problem of link prediction has attracted considerable recent attention from various domains such as sociology, anthropology, information science, and computer sciences. In this paper, we propose a link prediction algorithm based on ant colony optimization. By exploiting the swarm intelligence, the algorithm employs artificial ants to travel on a logical graph. Pheromone and heuristic information are assigned in the edges of the logical graph. Each ant chooses its path according to the value of the pheromone and heuristic information on the edges. The paths the ants traveled are evaluated, and the pheromone information on each edge is updated according to the quality of the path it located. The pheromone on each edge is used as the final score of the similarity between the nodes. Experimental results on a number of real networks show that the algorithm improves the prediction accuracy while maintaining low time complexity. We also extend the method to solve the link prediction problem in networks with node attributes, and the extended method also can detect the missing or incomplete attributes of data. Our experimental results show that it can obtain higher quality results on the networks with node attributes than other algorithms.