Link Prediction In Dynamic Networks Research Articles

A deep contrastive framework for unsupervised temporal link prediction in dynamic networks

In dynamic networks, temporal link prediction aims to predict the appearance and disappearance of links in future snapshots based on the network structure we have observed. It also plays a crucial role in network analysis and predicting the behavior of the dynamic system. However, most existing studies only focus on supervised temporal link prediction problems, i.e., taking part of the links in future snapshots as supervised information. The ones that can solve the unsupervised temporal link prediction problem are mainly based on matrix decomposition, which lack the capability to automatically extract nonlinear spatial and temporal features from dynamic networks. The most challenging part of this problem is to extract the inherent evolution of the patterns hidden in dynamic networks in unsupervised ways. Inspired by the application and achievement of contrastive learning in network representation learning, we propose a novel deep Contrastive framework for unsupervised Temporal Link Prediction (CTLP). Our framework is based on a deep encoder-decoder architecture, which can capture the nonlinear structure and temporal features automatically and can predict future links of subsequent snapshots of dynamic networks in an unsupervised manner. Besides, CTLP could handle the multi-step temporal link prediction problem of dynamic networks through attenuation modeling across the snapshots. Extensive experiments on temporal link prediction show that our CTLP framework significantly outperforms state-of-the-art unsupervised methods, and even outperforms the supervised methods in some cases.

Community-enhanced Link Prediction in Dynamic Networks

The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and identifying missing and future links in social networks. One of the well-known classes of methods in link prediction is a similarity-based method, which uses local and global topological information of the network to predict missing links. Some methods also exist based on quasi-local features to achieve a trade-off between local and global information on static networks. These quasi-local similarity-based methods are not best suited for considering community information in dynamic networks, failing to balance accuracy and efficiency. Therefore, a community-enhanced framework is presented in this article to predict missing links on dynamic social networks. First, a link prediction framework is presented to predict missing links using parameterized influence regions of nodes and their contribution in community partitions. Then, a unique feature set is generated using local, global, and quasi-local similarity-based as well as community information-based features. This feature set is further optimized using scoring-based feature selection methods to select only the most relevant features. Finally, four machine learning-based classification models are used for link prediction. The experiments are performed on six well-known dynamic networks and three performance metrics, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.

Dynamic network link prediction with node representation learning from graph convolutional networks

Dynamic network link prediction is extensively applicable in various scenarios, and it has progressively emerged as a focal point in data mining research. The comprehensive and accurate extraction of node information, as well as a deeper understanding of the temporal evolution pattern, are particularly crucial in the investigation of link prediction in dynamic networks. To address this issue, this paper introduces a node representation learning framework based on Graph Convolutional Networks (GCN), referred to as GCN_MA. This framework effectively combines GCN, Recurrent Neural Networks (RNN), and multi-head attention to achieve comprehensive and accurate representations of node embedding vectors. It aggregates network structural features and node features through GCN and incorporates an RNN with multi-head attention mechanisms to capture the temporal evolution patterns of dynamic networks from both global and local perspectives. Additionally, a node representation algorithm based on the node aggregation effect (NRNAE) is proposed, which synthesizes information including node aggregation and temporal evolution to comprehensively represent the structural characteristics of the network. The effectiveness of the proposed method for link prediction is validated through experiments conducted on six distinct datasets. The experimental outcomes demonstrate that the proposed approach yields satisfactory results in comparison to state-of-the-art baseline methods.

Deep Autoencoder-like non-negative matrix factorization with graph regularized for link prediction in dynamic networks

Link prediction in dynamic(temporal) networks refers to predicting future edges by analyzing the available network information. Among the existing temporal link prediction approaches, non-negative matrix factorization(NMF) is a kind of competitive algorithm and has attracted extensive attention. However, traditional NMF-based prediction methods are shallow methods and cannot fully mine the dynamic network, which may lead to a decrease in performance of algorithms. To overcome these shortcomings, inspired by deep Autoencoder, we propose two novel deep Autoencoder-like NMF with graph regularized prediction methods for dynamic networks. By fusing encoder component with deep structure into deep NMF model, our algorithms can sufficiently exploit the complex hierarchical information hidden in dynamic networks. To further extract the abundant information hidden in dynamic networks, graph regularization and PageRank are utilized to exploit the local and global topology information of each snapshot, respectively. By jointly optimizing them in deep Autoencoder-like NMF model, our model is able to preserve the local and global information hidden in dynamic networks, simultaneously. Moreover, an effective alternating iterative method with convergence guarantee is developed for minimizing the established model. Finally, we test our proposed prediction methods on several synthetic and real world datasets to demonstrate that our approaches outperform the state-of-the-art prediction approaches.

Link Prediction in Dynamic Networks Based on the Selection of Similarity Criteria and Machine Learning

The study’s findings showed that link prediction utilizing the similarity learning model in dynamic networks (LSDN) performed better than other learning techniques including neural network learning and decision tree learning in terms of the three criteria of accuracy, coverage, and efficiency., Compared to the random forest approach, the LSDN learning algorithm’s link prediction accuracy increased from 97% to 99%. The proposed method’s use of oversampling, which improved link prediction accuracy, was the cause of the improvement in area under the curve (AUC). To bring the ratio of the classes closer together, the suggested strategy attempted to produce more samples from the minority class. In addition, similarity criteria were chosen utilizing feature selection techniques based on correlation that had a strong link with classes. This technique decreased over-fitting and improved the suggested method’s test data generalizability. Based on the three criteria (accuracy, coverage, and efficiency), the research’s findings demonstrated that link prediction utilizing the similarity LSDN outperformed other learning techniques including neural network learning and decision tree learning. Compared to the random forest algorithm, the LSDN algorithm’s link prediction accuracy increased from 97% to 99%. The oversampling in the suggested strategy, which increased link prediction accuracy, is what caused the increase in AUC. To bring the ratio of the classes closer together, the suggested strategy attempted to produce more samples from the minority class. In addition, similarity criteria were chosen utilizing feature selection techniques based on correlation that had a strong link with classes. This technique decreased over-fitting and improved the suggested method’s test data generalizability.

A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks.

Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given's angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein-protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics.

PQKLP: Projected Quantum Kernel based Link Prediction in Dynamic Networks

Link prediction in dynamic networks finds new or future links based on the previously seen structure of the network. Its study is crucial to comprehending network evolution and its effects on individual nodes. Accuracy and efficiency of link prediction on dynamic networks are the two aspects research. We present Projected Quantum Kernel-based Link Prediction (PQKLP), a quantum-enhanced feature-based framework for solving link prediction problems in dynamic networks. According to our study, the Projected Quantum Kernel has not been utilized in the field of link prediction. Thus, we propose this method that combines the disciplines of social networks and quantum computing. We employed high-dimensional Hilbert spaces to enhance the prediction data in this model, which otherwise we only have access to via inner products provided by measurements. Such enhancement leads to better prediction results from machine learning-based link prediction techniques. We trained six classical machine learning models and their quantum-enhanced counterparts based on the enhanced features generated by the Projected Quantum Kernel (PQK) technique. The proposed model outperforms traditional link prediction methods, classical machine learning approaches, and current state-of-the-art methods on five well-known dynamic network datasets, as per the results of four performance metrics.

ComGCN: Community-Driven Graph Convolutional Network for Link Prediction in Dynamic Networks

Recent advances in deep learning have tremendously leveraged the performance of network representation learning (NRL). Multiple deep learning-based NRL models have been proposed recently to effectively handling primitive tasks of information network analysis and mining (INAM) domain, including link prediction (LP). LP is considered as an important one due to its multiple applications in many disciplines. In the recent few years, LP in dynamic networks has attracted a lot of attention from researchers to propose novel algorithms for better capturing both rich structural and evolutional information of complex information networks (INs). However, recent models are mainly concentrated on preserving the sequential representations of a given network over time. They have largely ignored other important structural features, such as: intracommunity which contributes to the creation of links between network nodes. In this article, we propose a novel community-driven dynamic NRL technique upon the RNN+GCN framework, called: ComGCN. Specifically, the ComGCN model is a combination of microscopic (node embedding-based) and mesoscopic (intracommunity-based) dynamic network embedding approach which enable effectively handling the LP problem in context of dynamism. Extensive experiments on real-world dynamic networks demonstrated the effectiveness of the proposed model compared with recent state-of-the-art baselines.

Gated graph convolutional network based on spatio-temporal semi-variogram for link prediction in dynamic complex network

Link prediction is one of the most important methods to uncover evolving mechanisms of dynamic complex networks. Determining these links raises well-known technical challenges in terms of weak correlation, uncertainty and non-stationary. In this paper, we presented a novel gated graph convolutional network (GCN) based on spatio-temporal semi-variogram (STEM-GCN). It learns spacial and temporal features in order to achieve link prediction in the dynamic networks. In this STEM-GCN model, we first utilized the spatio-temporal semi-variogram to obtain the spacial and temporal correlations from the dynamic networks. Its spacial correlation helped us determine the hyper-parameters of STEM-GCN and speed up its training. The correlation smoothing strategy is also introduced to eliminate the noise through temporal correlation and to improve the accuracy of link prediction. Finally, the network dynamics are captured by propagating the spacial and temporal features between consecutive time steps with stacked memory cell structures. The extensive experiments on real data sets demonstrated the effectiveness of the proposed approach for link prediction in dynamic complex networks.

CFLP: A new cost based feature for link prediction in dynamic networks

Dynamic networks are social networks in which node-to-node links contain a temporal component, i.e., node-to-node interactions over a specific time interval. As a result, the dynamic network’s structure changes over time, and previously connected nodes may or may not have an edge connecting them at any one time. The link prediction issue in dynamic networks aims to identify future network linkages based on the relative behavior of previous network updates. We present a feature-based solution that considers both individual snapshots and the overall network throughout the full-time span to answer the link prediction problem. We present a novel feature called Cost-based feature for link prediction (CFLP) for estimating edge behavior throughout the entire network, which uses a reward and penalty structure to summarize node activity across the entire network. We use similarity indices, classified into four major categories: local similarity, global similarity, quasi-local similarity, and clustering coefficient-based similarity, to measure edge activity in individual snapshots. We have also selected fourteen different snapshot-based features to find the most excellent combination of minimum features for link prediction. We used regression and mutual information-based scoring for feature selection to correctly quantify the relative effect of features among themselves and the overall link prediction problem. In order to give the best feasible solution to the link prediction problem, these individual features and their combinations were examined with five machine learning models. We employed five performance matrices – AUC, AUPR, Average Precision, F1, and Balance Accuracy Score – to compare the performance of our method to those of state-of-the-art approaches, and found that our method outperformed all.

PWAF : Path Weight Aggregation Feature for link prediction in dynamic networks

The growing popularity of online social networks is obvious these days, and it provides researchers with an opportunity to find solutions for a variety of practical applications. The approach of recognizing network structure and finding missing and future links in a social network is known as link prediction. On time-varying or dynamic networks, the link prediction problem has two major challenges: accuracy and efficiency. The improvement of accuracy in link prediction problem in dynamic networks is the objective of this research. The goal of this paper is to propose the Path Weight Aggregation Feature (PWAF), which is a new feature based on ranking multi edge occurrences across the entire network. Different topological aspects of the networks (Local, Global, and Quasi−local) as well as Clustering Coefficient based features are taken into consideration for feature generation, in addition to the suggested Path Weight-Based Aggregation Feature (PWAF). One of the features used for better prediction is the Level−2 node clustering coefficient (CCLP2). Different machine learning models, such as Neural Network (NN), Logistic Regression (LR), XGBoost (XGB), Random Forest Classifier (RFC), and linear Discriminant Analysis (LDA), are evaluated and verified for link prediction. The experiments are carried out on seven different well-known dynamic networks data sets in terms of five performance evaluation metrics, including AUPR, F1-score, AVG PRECISION, BAL ACC SCORE, and AUC, and the results show that our proposed method and its variants outperform state-of-the-art methods. The results demonstrate that PWAF−RFC is the best performing variation out of all machine learning classifiers we have experimented with.

Reciprocity, community detection, and link prediction in dynamic networks

Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference methods have been generalized to the dynamic case with the aim to model dynamic interactions. Particular interest has been devoted to extend the stochastic block model and its variant, to capture community structure as the network changes in time. While these models assume that edge formation depends only on the community memberships, recent work for static networks show the importance to include additional parameters capturing structural properties, as reciprocity for instance. Remarkably, these models are capable of generating more realistic network representations than those that only consider community membership. To this aim, we present a probabilistic generative model with hidden variables that integrates reciprocity and communities as structural information of networks that evolve in time. The model assumes a fundamental order in observing reciprocal data, that is an edge is observed, conditional on its reciprocated edge in the past. We deploy a Markovian approach to construct the network’s transition matrix between time steps and parameters’ inference is performed with an expectation-maximization algorithm that leads to high computational efficiency because it exploits the sparsity of the dataset. We test the performance of the model on synthetic dynamical networks, as well as on real networks of citations and email datasets. We show that our model captures the reciprocity of real networks better than standard models with only community structure, while performing well at link prediction tasks.

Features fusion based link prediction in dynamic neworks

Link prediction is the process of detecting new or missing links in any network. This work focuses on link prediction in dynamic networks. One of the major challenges of the link prediction problem is to achieve high accuracy. One of the well-known methods for link prediction is the similarity-based method, which uses similarity-based score. The three widely used similarity-based indices are Local (L), Global (G), and Quasi-local (Q) for calculating similarity scores. In this work, we have proposed a model, namely the LGQ model. In our LGQ model, the wide categories of indices are used in different combinations(L, G, Q, LG, LQ, GQ, LGQ) for feature set generation that can be used with various machine learning techniques for link prediction. In local similarity indices, we have considered Common Neighbors (CN), Adamic/Adar Index (AA), Jaccard Coefficient (JC), and Preferential Attachment (PA). In global similarity indices, we have considered cos+, Average Commute Time (ACT), Shortest Path (SP), MFI (Matrix Forest Index), and in quasi-local indices, local path Index (LP), Path of Length 3 (L3). We have tested and validated our proposed model by conducting numerical simulations on six well-known dynamic network datasets. The results demonstrate that the proposed LGQ model and its variations outperform several baseline algorithms in terms of AUPR, F1 score, Balanced accuracy score (BAC), and AUC. We also demonstrate and contrast the preference of Neural Network and Xgboost based prediction models for different variations of proposed feature sets.

Network embedding based link prediction in dynamic networks

Link prediction is a fundamental task in network theory due to the wide variety of applications in different domains. The objective of link prediction is to find the future links that are likely to be seen in some future time. In this paper, we propose a novel embedding-based technique that utilizes the concept of the Skip-gram framework. An embedding-based method embodies the learning of feature representations of nodes or links in a network. Our method jointly exploits the Skip-gram framework and max aggregator for edge embedding tasks. To test the effectiveness of the proposed method, we have conducted experiments on large size real-world networks. In the experimental evaluation, we have compared the proposed method against both similarity-based and learning-based approaches. The experimental results indicate the effectiveness of the proposed method both in terms of time and accuracy.

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.

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link prediction of static network, while real-world networks always evolve over time with the occurrence and vanishing of nodes and links. Dynamic network link prediction (DNLP) thus has been attracting more and more attention since it can better capture the evolution nature of networks, but still most algorithms fail to achieve satisfied prediction accuracy. Motivated by the excellent performance of long short-term memory (LSTM) in processing time series, in this article, we propose a novel encoder-LSTM-decoder (E-LSTM-D) deep learning model to predict dynamic links end to end. It could handle long-term prediction problems, and suits the networks of different scales with fine-tuned structure. To the best of our knowledge, it is the first time that LSTM, together with an encoder-decoder architecture, is applied to link prediction in dynamic networks. This new model is able to automatically learn structural and temporal features in a unified framework, which can predict the links that never appear in the network before. The extensive experiments show that our E-LSTM-D model significantly outperforms newly proposed DNLP methods and obtain the state-of-the-art results.

An Advanced Deep Generative Framework for Temporal Link Prediction in Dynamic Networks.

Temporal link prediction in dynamic networks has attracted increasing attention recently due to its valuable real-world applications. The primary challenge of temporal link prediction is to capture the spatial-temporal patterns and high nonlinearity of dynamic networks. Inspired by the success of image generation, we convert the dynamic network into a sequence of static images and formulate the temporal link prediction as a conditional image generation problem. We propose a novel deep generative framework, called NetworkGAN, to tackle the challenging temporal link prediction task efficiently, which simultaneously models the spatial and temporal features in the dynamic networks via deep learning techniques. The proposed NetworkGAN inherits the advantages of the graph convolutional network (GCN), the temporal matrix factorization (TMF), the long short-term memory network (LSTM), and the generative adversarial network (GAN). Specifically, an attentive GCN is first designed to automatically learn the spatial features of dynamic networks. Second, we propose a TMF enhanced attentive LSTM (TMF-LSTM) to capture the temporal dependencies and evolutionary patterns of dynamic networks, which predicts the network snapshot at next timestamp based on the network snapshots observed at previous timestamps. Furthermore, we employ a GAN framework to further refine the performance of temporal link prediction by using a discriminative model to guide the training of the deep generative model (i.e., TMF-LSTM) in an adversarial process. To verify the effectiveness of the proposed model, we conduct extensive experiments on five real-world datasets. Experimental results demonstrate the significant advantages of NetworkGAN compared to other strong competitors.

Link prediction in dynamic networks using time-aware network embedding and time series forecasting

As most real-world networks evolve over time, link prediction over such dynamic networks has become a challenging issue. Recent researches focus towards network embedding to improve the performance of link prediction task. Most of the network embedding methods are only applicable to static networks and therefore cannot capture the temporal variations of dynamic networks. In this work, we propose a time-aware network embedding method which generates node embeddings by capturing the temporal dynamics of evolving networks. Unlike existing works which use deep architectures, we design an evolving skip-gram architecture to create dynamic node embeddings. We use the node embedding similarities between consecutive snapshots to construct a univariate time series of node similarities. Further, we use times series forecasting using auto regressive integrated moving average (ARIMA) model to predict the future links. We conduct experiments using dynamic network snapshot datasets from various domains and demonstrate the advantages of our system compared to other state-of-the-art methods. We show that, combining network embedding with time series forecasting methods can be an efficient solution to improve the quality of link prediction in dynamic networks.

Link prediction in dynamic networks based on the attraction force between nodes

As an important technology of social network analysis, link prediction is widely applied in computer science and many other fields. Link prediction can be used to detect missing links or predict whether two unconnected nodes will connect in the future. Various link prediction approaches have been proposed based on similarity metrics or learning in recent years; however, most failed to consider the direct changes during network development, and hence they are not applied to dynamic networks whose structures change continuously over time. In this paper, a novel approach for link prediction in dynamic networks based on the attraction force between nodes (DLPA) is proposed for detecting missing links and for predicting whether potential links will become real links in the future. First, a level is assigned to each node, which is used to represent the influence strength of the node compared to its neighbours in the initial network snapshot. The level must be updated with changes in the nodes. Then, the connection probability of each potential link is calculated based on the levels of the corresponding nodes and the attraction force between them. Thus, missing links can be detected and potential links can be predicted. In addition, the connection probabilities of potential links calculated via the proposed approach can vary with the evolution of the network. Experiments on static and dynamic real-world networks are conducted to evaluate the performance of the proposed approach, and the results demonstrate that the proposed approach outperforms several baseline algorithms in terms of prediction accuracy.

DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization

A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correctness of these algorithms. Finally, we demonstrate how latent NMF features can express network dynamics efficiently rather than by static representation, thereby yielding better performance. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms.