Link prediction in complex networks using information-theoretic measures

Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms can be applied, being less or more effective in the relevant scenarios. In this work, we develop a novel framework that attempts to compose the best features of link prediction algorithms when applied to a network, in order to have even more reliable predictions, especially in topologies emerging in Industrial Internet of Things (IIoT) environments. According to the proposed framework, we first apply appropriate link prediction algorithms that we have chosen for an analyzed network (basic algorithms). Each basic algorithm gives us a numerical estimate for each missing edge in the network. We store the results of each basic algorithm in appropriate structures. Then we provide them as input to a developed genetic algorithm. The genetic algorithm evaluates the results of the basic algorithms for each missing edge of the network. At each missing edge of the network and from generation to generation, it composes the estimates of the basic algorithms regarding each edge and produces a new optimized estimate. This optimization results in a vector of weights where each weight corresponds to the effectiveness of the prediction for each of the basic algorithms we have employed. With these weights, we build a new enhanced predictor tool, which can obtain new optimized estimates for each missing edge in the network. The enhanced predictor tool applies to each missing edge the basic algorithms, normalizes the basic algorithms’ estimates, and, using the weights of the estimates derived from the genetic algorithm, returns a new estimate of whether or not an edge will be added in the future. According to the results of our experiments on several types of networks with five well-known link prediction algorithms, we show that the new enhanced predictor tool yields in every case better predictions than each individual algorithm, therefore providing an accuracy-targeting alternative in the existing state of the art.

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.

Link prediction is one of the most important and challenging tasks in complex network analysis, which aims to predict the existence of unknown links based on the known information in the network. As critical topological properties in the network, node’s degree and clustering coefficient are well-suited for describing the tightness of connection between nodes. The importance of node can affect the possibility of link existence to a certain extent. By analyzing the impact of different centrality on links, which concluded that the degree centrality and proximity centrality have the greatest influence on network link prediction. A link prediction algorithm combines importance of node and network topological properties, called DCCLP, is proposed in this paper, the symmetry of the adjacency matrix is considered in the DCCLP link prediction algorithm to further describe the structural similarity of network nodes. In the training phase of the DCCLP algorithm, the maximized AUC indicator in the training set as the objective, and the optimal parameters are estimated by utilizing the White Shark Optimization algorithm. Then the prediction accuracy of the DCCLP algorithm is evaluated in the test set. By experimenting on twenty-one networks with different scales, and comparing with existing algorithms, the experimental results show that the effectiveness and feasibility of DCCLP algorithm, and further illustrate the importance of the degree centrality of node pairs and proximity centrality of nodes to improve the prediction accuracy of link prediction.

Link prediction in complex networks aims to mine hidden or to-be-generated links between network nodes, which plays a significant role in fields such as the cold start of recommendation systems, knowledge graph completion and biomedical experiments. The existing link prediction models based on graph neural networks, such as graph convolution neural networks, often only learn the low-frequency information reflecting the common characteristics of nodes while ignoring the high-frequency information reflecting the differences between nodes when learning node representation, which makes the corresponding link prediction models show over smoothness and poor performance. Focusing on links in complex networks, this paper proposes an edge convolutional graph neural network EdgeConvHiF that fuses high-frequency node information to achieve the representation learning of links so that link prediction can be realized by implementing the classification of links. EdgeConvHiF can also be employed as a baseline, and extensive experiments on real-world benchmarks validate that EdgeConvHiF not only has high stability but also has more advantages than the existing representative baselines.

Link prediction aims to identify missing links within static networks or estimate the probability of emerging links in dynamic networks, representing a critical and challenging research direction in complex network. Many similarity-based methods have been established from various viewpoints, however, there are relatively few methods that take into account both the path information between node pairs and the nodes along the path. To fill this gap, we propose two novel link prediction algorithms, namely LPRA and LPH. The core idea is that the similarity between node pairs is closely related to the local paths connecting the two nodes and the resource transition capability of the nodes along those paths. The algorithms utilize the local paths with adjustable lengths between node pairs and the topological information of the nodes on the path to calculate the similarity index, which incorporate the resource transition capabilities of all the nodes on the possible paths between node pairs. We conducted multiple groups of comparative experiments on 10 real-world networks to validate the effectiveness of the proposed algorithm. Experimental results demonstrate that LPRA and LPH outperform nine classical methods and five recently popular methods. Moreover, LPRA demonstrates a significant difference level (P-value [Formula: see text]) compared to most of the methods in the ANOVA of AUC accuracy, indicating a statistically significant performance advantage of LPRA.

Link prediction is an essential difficulty with a wide range of applications in several complex networks such as Facebook and LinkedIn. Link Prediction is used as an essential feature in these networks. A co-authorship network is used in this paper. The main focus is the Academic Social Networks link prediction. Links indicate the association of researchers from the network who may be called the network of co-authors. In such networks, nodes represent the authors and a connection (link) between them indicates that they share this product (research or paper) as author and coauthor. The connections between researchers are more significant than individual effort for effective results in the field of study. For predicting interconnections with a given level of accuracy, a data collection including additional information about the authors and their articles can be utilized. This paper model the effective LinkGyp prediction technology. Python and C# programming languages are used in implementing and evaluating this paper using Precision-Recall and area under the curve (AUC) measures. The final results on the co-authorship data set show the accuracy of the LinkGyp technique for Link Prediction.

Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.

Link prediction is an open problem in the complex network, which attracts much research interest currently. However, little attention has been paid to the relation between network structure and the performance of prediction methods. In order to fill this vital gap, we try to understand how the network structure affects the performance of link prediction methods in the view of clustering. Our experiments on both synthetic and real-world networks show that as the clustering grows, the precision of these methods could be improved remarkably, while for the sparse and weakly clustered network, they perform poorly. We explain this through the distinguishment caused by increased clustering between the score distribution of positive and negative instances. Our finding also sheds light on the problem of how to select appropriate approaches for different networks with various densities and clusterings.

Robust non-negative matrix factorization for link prediction in complex networks using manifold regularization and sparse learning

Link prediction via ranking with a multiple membership nonparametric Bayesian model by Chan Yun Kwan thesis 2012 ix, 43 p. : ill. ; 30 cm Link prediction in complex networks has found applications in a wide range of real-world domains…Read more ›

The study of complex networks has attracted the attention of many research in recent years due to their potential to describe complex systems. Identifying potential missing links in networks has a wide range of applications. In this paper, we propose a new approach for solving link prediction problem based on a hidden variables model. Recent studies suggest that real networks have underlying hidden metric space that explain their structural characteristics. We suggest to model the link prediction as an inverse problem in order to infer node coordinates in a hidden metric space and subsequently use the produced model to find the missing links and detect the spurious ones. Our results show that the proposed approach can provide more accurate prediction than other existing methods.

Link prediction (LP) in complex networks has gained a lot of popularity in recent times. Many real-life problems modelled as complex networks require link prediction to know about the future modifications about the network. LP can be used to analyse and solve various critical and interesting problems like suggesting alternate routes for navigation in the transportation network, predicting an outbreak of a disease in disease network, detecting spam emails, controlling privacy in human communication network etc. Several approaches explored in the field of link prediction include the topological and non-topological feature, based on supervised and unsupervised learning methods etc. These techniques often require large computing power and time due to which they become inefficient as the network grows. In this paper, we propose a method that can be scaled to large complex networks by giving more emphasis to the local features and employing parallel processing for faster extraction of local features. We extend the nature-inspired method of gravitational force for link prediction replacing mass and distance by various centrality and dissimilarity measures, respectively. We also introduce various similarity and popularity measures and then experimentally demonstrate that using gravitational approach along with our proposed measures outperform the earlier methods of link prediction that uses traditional methods of centrality and similarity.KeywordsLink prediction (LP)Complex networksNature-inspired network predictive modelNewtonian Gravitational law

The link prediction in complex networks is about how to predict the possibility about the connection of unknown nodes via analyzing known nodes inside the network. Considering the current needs of the education websites as the starting point, this paper introduces the specific content of the link prediction based on detailed link-node similarity prediction methods. And this paper describes the measurement accuracy of the node similarity. This provides a guideline for the development of web recommendation systems.

Robust link prediction in criminal networks: A case study of the Sicilian Mafia

Link prediction is the problem of predicting the location of either unknown or fake links from uncertain structural information of a network. Link prediction algorithms are useful in gaining insight into different network structures from partial observations of exemplars. However, existing link prediction algorithms only focus on regular complex networks and are overly dependent on either the closed triangular structure of networks or the so-called preferential attachment phenomenon. The performance of these algorithms on highly sparse or treelike networks is poor. In this letter, we proposed a method that is based on the network heterogeneity. We test our algorithms for three real large sparse networks: a metropolitan water distribution network, a Twitter network, and a sexual contact network. We find that our method is effective and performs better than traditional algorithms, especially for the Twitter network. We further argue that heterogeneity is the most obvious defining pattern for complex networks, while other statistical properties failed to be predicted. Moreover, preferential attachment based link prediction performed poorly and hence we infer that preferential attachment is not a plausible model for the genesis of many networks. We also suggest that heterogeneity is an important mechanism for online information propagation.