Link Prediction In Networks Research Articles

A novel recovery strategy based on link prediction and hyperbolic geometry of complex networks

AbstractRecovery of complex networks is an important issue that has been extensively used in various fields. Much work has been done to measure and improve the stability of complex networks during attacks. Recently, many studies have focused on the network recovery strategies after attack. In many real cases, link retrieval and recovery of critical infrastructures such as transmission network and telecommunications infrastructures are of particular importance and should be prioritized. For example, when a flood disrupts optical fibre communications in transmission networks and paralyzes the network, link retrieval corresponds to the recovery of fibre communications, so that the transmission network communication capacity can be restored at the earliest possible time. So, predicting the appropriate reserved links in a way that the network can be recovered at the lowest cost and fastest time after attacks or interruptions will be critical in a disaster. In this article, different kinds of attack strategies are provided and some retrieval strategies based on link prediction methods are proposed to recover the network after failure and attack. Beside that, a new link prediction method based on the hyperbolic geometry of the complex network is proposed to discover redundant links. The numerical simulations reveal its superiority than other common and recent link prediction-based methods used for network recovery, especially in the case of attacks based on edge betweenness strategy.

HM-EIICT: Fairness-aware link prediction in complex networks using community information

The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

Network Embedding For Link Prediction in Bipartite Networks

Many social networks have a bipartite nature. Link prediction in social networks has been the focus of interest for many researchers recently. Network embedding, which maps each node in the network to a low-dimensional feature vector is used to solve many problems. The aim of this study is to investigate how network embedding enhance the link prediction performance in bipartite networks. A network embedding and a supervised learning based link prediction model has been presented for bipartite networks. The input of the supervised learning model is learned embedding vectors of node pairs obtained from network embedding method. The target feature of prediction is a binary label indicating the existence or absence of a link between these node pairs. Ensemble learning algorithms have been applied for supervised link prediction. The experiments performed on two bipartite social networks built from public datasets led promising results with 0.939 and 0.974 AUC values. Random Forest models trained with embedding vectors obtained from BiNE method achieved the highest performances.

Supervised temporal link prediction in large-scale real-world networks

Link prediction is a well-studied technique for inferring the missing edges between two nodes in some static representation of a network. In modern day social networks, the timestamps associated with each link can be used to predict future links between so-far unconnected nodes. In these so-called temporal networks, we speak of temporal link prediction. This paper presents a systematic investigation of supervised temporal link prediction on 26 temporal, structurally diverse, real-world networks ranging from thousands to a million nodes and links. We analyse the relation between global structural properties of each network and the obtained temporal link prediction performance, employing a set of well-established topological features commonly used in the link prediction literature. We report on four contributions. First, using temporal information, an improvement of prediction performance is observed. Second, our experiments show that degree disassortative networks perform better in temporal link prediction than assortative networks. Third, we present a new approach to investigate the distinction between networks modelling discrete events and networks modelling persistent relations. Unlike earlier work, our approach utilises information on all past events in a systematic way, resulting in substantially higher link prediction performance. Fourth, we report on the influence of the temporal activity of the node or the edge on the link prediction performance, and show that the performance differs depending on the considered network type. In the studied information networks, temporal information on the node appears most important. The findings in this paper demonstrate how link prediction can effectively be improved in temporal networks, explicitly taking into account the type of connectivity modelled by the temporal edge. More generally, the findings contribute to a better understanding of the mechanisms behind the evolution of networks.

Explainable link prediction based on multi-granularity relation-embedded representation

Existing link prediction methods focus on mining relations of nodes in terms of network structure, ignoring rich attributes of nodes. In the micro-blog social networks, text contents describe users’ diverse behaviors, which depicts ones’ multi-dimensional and multi-granularity preferences. Thus, based on users’ multi-granularity interest, we can predict and explain social relations among users. In this work, we develop an explainable social relation extraction method based on hierarchical semantic specificity matrices of user interest. First, according to users’ micro-blog contents, we model user interest subjects and design their semantic specificity matrices. Then, on the basis of interest subjects, we learn multi-dimensional and multi-granularity semantic specificity matrices with translation mechanism, which reflects user pairs’ follow relation causes from multiple aspects. Finally, by leveraging multi-granularity semantic specificity matrices, we predict and explain users’ social follow relation. To justify our proposal, we conduct extensive experiments on Sina Weibo and Tencent Weibo datasets. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods in Hits, MeanRank and MRR metrics. The proposal can effectively predict users’ follow relation from the aspects of multi-granularity interest subjects, and improve the accuracy of link prediction in social networks.

Transfer learning based attack detection for wireless communication networks

AbstractWireless communication network attack detection is important in the field of wireless communication network data mining. Transfer learning is a powerful tool to solve the problem of link prediction in unlabeled wireless networks. In the feature mapping process of transfer learning, the loss of source domain information is very serious, which leads to the loss of knowledge in inverse mapping. To obtain more specific and complete information in the source domain, a link prediction method based on the distribution function fitting method of domain orthogonal term family is proposed. The key samples in the source domain are used to construct a family of orthogonal polynomials, and the least square approximation of the distribution function is obtained by using the family of orthogonal polynomials. The feature vector set of the key samples is extracted from the source domain, and the pseudo label is assigned to the target training set, so that the link classifier can complete the supervised link prediction task. In addition, an advanced performance of TLAD is obtained by comparing with other state‐of‐the‐art models in practice.

Link prediction in dynamic networks using random dot product graphs

The problem of predicting links in large networks is an important task in a variety of practical applications, including social sciences, biology and computer security. In this paper, statistical techniques for link prediction based on the popular random dot product graph model are carefully presented, analysed and extended to dynamic settings. Motivated by a practical application in cyber-security, this paper demonstrates that random dot product graphs not only represent a powerful tool for inferring differences between multiple networks, but are also efficient for prediction purposes and for understanding the temporal evolution of the network. The probabilities of links are obtained by fusing information at two stages: spectral methods provide estimates of latent positions for each node, and time series models are used to capture temporal dynamics. In this way, traditional link prediction methods, usually based on decompositions of the entire network adjacency matrix, are extended using temporal information. The methods presented in this article are applied to a number of simulated and real-world graphs, showing promising results.

Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks

Matrix completion based collaborative filtering is considered scalable and effective for online service link prediction (e.g., movie recommendation) but does not meet the challenges of link prediction in ecological networks. A unique challenge of ecological networks is that the observed data are subject to systematic imperfect detection, due to the difficulty of accurate field sampling. In this work, we propose a new framework customized for ecological bipartite network link prediction. Our approach starts with incorporating the Poisson N-mixture model, a widely used framework in statistical ecology for modeling imperfect detection of a single species in field sampling. Despite its extensive use for single species analysis, this model has never been considered for link prediction between different species, perhaps because of the complex nature of both link prediction and N-mixture model inference. By judiciously combining the Poisson N-mixture model with a probabilistic nonnegative matrix factorization (NMF) model in latent space, we propose an intuitive statistical model for the problem of interest. We also offer a scalable and convergence-guaranteed optimization algorithm to handle the associated maximum likelihood identification problem. Experimental results on synthetic data and two real-world ecological networks data are employed to validate our proposed approach.

A Social Network Link Prediction Method Based on Stacked Generalization

Abstract Traditional link prediction methods of social network are vulnerable to the influence of network structure and have poor generalization, and only on a small number of networks and evaluation indicators. To improve the stability and accuracy of link prediction, this paper assembles 15 similarity indexes, introduces the idea of stacking into the link prediction of complex networks, and presents a link prediction method (Logistic-regression LightGBM Stacking Link Prediction, LLSLP). Firstly, social network link prediction is regarded as a binary classification problem. Secondly, the hyper parameters of the basic model are determined by using cross-validation and grid searching; thirdly, Logistic-regression and LightGBM are integrated by stacked generalization; Finally, take 10 different networks as practical examples. The feasibility and effectiveness of the proposed method are verified by comparing 7 evaluation indicators. The experimental results show that: the proposed method is not only more than 98.71% higher than the traditional CN (Common Neighbor) and other models are 10.52% higher on average. In addition, compared with the traditional 15 link prediction algorithms, $F1- score$ value and $MCC$ (Matthews Correlation Coefficient) value is increased by 3.2% ~ 9.7% and 5.9% ~ 14% respectively. The proposed method has good accuracy and generalization. It can also be applied to recommendation system.

Learning-based link prediction analysis for Facebook100 network

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data has significantly contributed to the evolution of social network research and link prediction techniques, which are important tools in link mining and analysis. This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We stu- dy performance and evaluate multiple machine learning algorithms on different feature sets. To derive the features, we use network embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. In addition, we also employ node- -based features, which are available for the Facebook100 network, though rarely found in other datasets. The adopted approaches are discussed and results are clearly presented. Lastly, we compare and review the applied models, where overall performance and classification rates are presented.

Learning-based link prediction analysis for Facebook100 network

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data has significantly contributed to the evolution of social network research and link prediction techniques, which are important tools in link mining and analysis. This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We stu- dy performance and evaluate multiple machine learning algorithms on different feature sets. To derive the features, we use network embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. In addition, we also employ node- -based features, which are available for the Facebook100 network, though rarely found in other datasets. The adopted approaches are discussed and results are clearly presented. Lastly, we compare and review the applied models, where overall performance and classification rates are presented.

A new method for predicting future links in temporal networks based on node influence

Link prediction in temporal networks has always been a hot topic in both statistical physics and network science. Most existing works fail to consider the inner relationship between nodes, leading to poor prediction accuracy. Even though a wide range of realistic networks are temporal ones, few existing works investigated the properties of realistic and temporal networks. In this paper, we address the problem of abstracting individual attributes and propose a adaptive link prediction method for temporal networks based on [Formula: see text]-index to predict future links. The matching degree of nodes is first defined considering both the native influence and the secondary influence of local structure. Then a similarity index is designed using a decaying parameter to punish the snapshots with their occurring time. Experimental results on five realistic temporal networks observing consistent gains of 2–9% AUC in comparison to the best baseline in four networks show that our proposed method outperforms several benchmarks under two standard evaluation metrics: AUC and Ranking score. We also investigate the influence of the free parameter and the definition of matching degree on the prediction performance.

A new link prediction in multiplex networks using topologically biased random walks

Link prediction is a technique to forecast future new or missing relationships between nodes based on the current network information. However, the link prediction in monoplex networks seems to have a long background, the attempts to accomplish the same task on multiplex networks are not abundant, and it was often a challenge to apply conventional similarity methods to multiplex networks. The issue of link prediction in multiplex networks is the way of predicting the links in one layer, taking structural information of other layers into account. One of the most important methods of link prediction in a monoplex network is a local random walk (LRW) that captures the network structure using pure random walking to measure nodes similarity of the graph and find unknown connections. The goal of this paper is to propose an extended version of local random walk based on pure random walking for solving link prediction in the multiplex network, referred to as the Multiplex Local Random Walk (MLRW). We explore approaches for leveraging information mined from inter-layer and intra-layer in a multiplex network to define a biased random walk for finding the probability of the appearance of a new link in one target layer. Experimental studies on seven multiplex networks in the real world demonstrate that a multiplex biased local random walk performs better than the state-of-the-art methods of link prediction and corresponding unbiased case and improves prediction accuracy.

Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks

Link and sign prediction in complex networks bring great help to decision-making and recommender systems, such as in predicting potential relationships or relative status levels. Many previous studies focused on designing the special algorithms to perform either link prediction or sign prediction. In this work, we propose an effective model integration algorithm consisting of network embedding, network feature engineering, and an integrated classifier, which can perform the link and sign prediction in the same framework. Network embedding can accurately represent the characteristics of topological structures and cooperate with the powerful network feature engineering and integrated classifier can achieve better prediction. Experiments on several datasets show that the proposed model can achieve state-of-the-art or competitive performance for both link and sign prediction in spite of its generality. Interestingly, we find that using only very low network embedding dimension can generate high prediction performance, which can significantly reduce the computational overhead during training and prediction. This study offers a powerful methodology for multi-task prediction in complex networks.

Supervised-learning link prediction in single layer and multiplex networks

The emergence of complex real-world networks has put forth a plethora of information about different domains. Link-prediction is one of the emerging research problems that utilizes the information from the networks to find future relationships between the nodes. The structure of real-world networks varies from having homogeneous relationships to having multiple associations. The homogeneous relationships are modeled by single-layer networks, while the multiplex networks represent the multiple associations. This study proposes a solution for finding future links in single-layer and multiplex networks by using supervised machine learning techniques. This study considers a set of topological features of the network for training the machine learning classifiers. The training and testing data set construction framework devised in this work helps in evaluating the proposed method on different networks. This study also contributes towards identifying four community-based features for the proposed mechanism.

An information theoretic approach to link prediction in multiplex networks

The entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

Node similarity-based graph convolution for link prediction in biological networks.

Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing attention in link prediction. An important component of GCN-based network embedding is the convolution matrix, which is used to propagate features across the network. Existing algorithms use the degree-normalized adjacency matrix for this purpose, as this matrix is closely related to the graph Laplacian, capturing the spectral properties of the network. In parallel, it has been shown that GCNs with a single layer can generate more robust embeddings by reducing the number of parameters. Laplacian-based convolution is not well suited to single-layered GCNs, as it limits the propagation of information to immediate neighbors of a node. Capitalizing on the rich literature on unsupervised link prediction, we propose using node similarity-based convolution matrices in GCNs to compute node embeddings for link prediction. We consider eight representative node-similarity measures (Common Neighbors, Jaccard Index, Adamic-Adar, Resource Allocation, Hub- Depressed Index, Hub-Promoted Index, Sorenson Index and Salton Index) for this purpose. We systematically compare the performance of the resulting algorithms against GCNs that use the degree-normalized adjacency matrix for convolution, as well as other link prediction algorithms. In our experiments, we use three-link prediction tasks involving biomedical networks: drug-disease association prediction, drug-drug interaction prediction and protein-protein interaction prediction. Our results show that node similarity-based convolution matrices significantly improve the link prediction performance of GCN-based embeddings. As sophisticated machine-learning frameworks are increasingly employed in biological applications, historically well-established methods can be useful in making a head-start. Our method, SiGraC, is implemented as a Python library and is freely available at https://github.com/mustafaCoskunAgu/SiGraC.

PILHNB: Popularity, interests, location used hidden Naive Bayesian-based model for link prediction in dynamic social networks

Link prediction aims to predict the missing interactions in evolving networks that may appear in the future. It has practical importance in various real-world applications, ranging from friendship recommendation, knowledge graph completion, target advertising, and protein–protein interaction prediction. Most of the recent efforts focus on the structure of the network while ignoring many other essential factors. In this paper, we present a modified Latent Dirichlet Allocation (LDA), and Hidden Naive Bayesian (HNB) based link prediction technique named PILHNB model for link prediction in dynamic social networks by considering behavioral controlling elements like relationship network structure, nodes’ attributes, location-based information of nodes, nodes’ popularity, users’ interests, and learning the evolution pattern of these factors in the networks. Experimental results on six real-world networks demonstrate our proposed models’ effectiveness and efficiency compared with existing state-of-the-art link prediction techniques.

Exploring an edge convolution and normalization based approach for link prediction in complex networks

Link prediction in complex networks is to discover hidden or to-be-generated links between network nodes. Most of the mainstream graph neural network (GNN) based link prediction methods mainly focus on the representation learning of nodes, and are prone to over-smoothing problem. This paper dedicates to the representation learning of links, and designs an edge convolution operation so as to realize the link representation learning. Besides, we propose an normalization strategy for the learned link representation, for the purpose of alleviating the over-smoothing problem of edge convolution based link prediction model, when constructing the link prediction graph neural network EdgeConvNorm with stacking edge convolution manipulations. Lastly, we employ a binary classifier sigmod on the Hadamard product of two nodes representation parsed from the final learned link representation. The EdgeConvNorm can also be employed as a baseline, and extensive experiments on real-world benchmark complex networks validate that EdgeConvNorm not only alleviates the over-smoothing problem, but also has advantages over representative baselines.

Scale-free Characteristics and Link Prediction in Complex Railway Network

The link-prediction of complex networks is a new method of network structure mining, which is different from the traditional methods based on machine learning. It reveals those existing relations and predicts possible ones, by using the similarity index of network structure. This paper studied the topology of the railway and link-prediction. The railway dataset was firstly obtained by sampling and pre-processing. Its degree obeys the power-law distribution with the scale-free characteristics. Then, we give some predictions for railway networks. These tests reached good prediction accuracy. The research result has demonstrated that the topological structure of network could be better choice to the link-prediction of a real complex networks.