In this paper, a link gain prediction-based power control scheme is designed for DS-CDMA cellular mobile systems. The link gain prediction can help to remove the interference of the power control adjustment itself. The pipeline recurrent neural network (PRNN) with extended recursive least square (ERLS) is adopted for the prediction. This PRNN/ERLS predictor possesses infinite memory of past sig...

The problem of link prediction has attracted considerable recent attention from various domains such as sociology, anthropology, information science, and computer sciences. A link prediction algorithm is proposed based on link similarity score propagation by a random walk in networks with nodes attributes. In the algorithm, each link in the network is assigned a transmission probability accordi...

Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many important algorithmic problems on dynamic networks, including link prediction. Link prediction seeks to predict edges that will be added to the network ...

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous li...

Link prediction is a technique that uses the topological information in a given network to infer the missing links in it. Since past research on link prediction has primarily focused on enhancing performance for given empirical systems, negligible attention has been devoted to link prediction with regard to network models. In this paper, we thus apply link prediction to two network models: The ...

The effects of social influence and network autocorrelation suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference. However, the algorithmic question of how to efficiently incorporate these two sources of information remains largely unanswered. We propose a Social-Attribute Network (SAN) model that gracefully in...

Link prediction is a fundamental problem with applications in many fields ranging from biology to computer science. In the literature, most effort has been devoted to estimate the likelihood of the existence of a link between two nodes, based on observed links and nodes' attributes in a network. In this paper, we apply several representative link prediction methods to reconstruct the network, n...

In the recent years, reciprocal link prediction has received some attention from the data mining and social network analysis researchers, who solved this problem as a binary classification task. However, it is also important to predict the interval time for the creation of reciprocal link. This is a challenging problem for two reasons: First, the lack of effective features, because well-known l...

So far, many network-structure-based link prediction methods have been proposed. However, these traditional methods were proposed by highlighting one or two structural features of networks, and then use the methods to implement link prediction in different networks. In many cases, the performance is not ideal since each network has its unique underlying structural features. In this article, by ...

Link prediction is a link mining task that tries to find new edges within a given graph. Among the targets of link prediction there is large directed graphs, which are frequent structures nowadays. The typical sparsity of large graphs demands of high precision predictions in order to obtain usable results. However, the size of those graphs only permits the execution of scalable algorithms. As a...